Correct:

D. By automated signal detection jobs KPI Signals supports notifications through automated jobs that continuously monitor indicator scores for statistical patterns such as outliers, short runs, and long runs. These detection jobs run on a scheduled basis and trigger notifications when a signal is identified. This allows Performance Analytics administrators and stakeholders to receive timely alerts about significant changes or anomalies in KPI behavior without manual intervention. The automation ensures consistent monitoring and proactive insight delivery.

Incorrect:

A. By setting auto-reply responses Auto-reply responses are related to email or messaging systems and have no connection to KPI Signals. KPI Signals does not use or configure auto-replies as part of its notification mechanism. This option is unrelated to how signals are detected or communicated.

B. By forwarding email notifications While KPI Signals can generate notifications, it does not rely on manual email forwarding. Notifications are system-generated and sent based on configured rules and signal detection outcomes. Forwarding emails is a user-level action and not part of the KPI Signals framework.

C. Through regular back-ups Back-ups are part of system maintenance and data recovery processes. They do not play any role in signal detection or notification delivery. KPI Signals operates independently of backup routines and focuses on real-time performance monitoring.

pa_spotlight role is enough

Correct:

A. KPI Details
In Agent Workspace, the KPI Details view provides the same analytical depth as the Analytics Hub in the legacy UI. It allows users to:

View indicator values over time

Analyze breakdowns and thresholds

Access historical trends and comparisons

Use case: A service manager can open KPI Details for “Open Incidents” to see performance over the last 30 days, compare against targets, and apply breakdowns like assignment group.

Incorrect:

B. Analytics Q&A
Why it‘s incorrect: Analytics Q&A is a natural language query tool that helps users find indicators and data sources. It does not replicate Analytics Hub’s visualization or drilldown capabilities.

C. Analytics Center
Why it‘s incorrect: Analytics Center is a centralized dashboarding and configuration interface, not a direct replacement for Analytics Hub. It’s used for managing indicators and widgets, not for interactive analysis.

D. KPI Signals
Why it‘s incorrect: KPI Signals detect anomalies or significant changes in indicator values. While useful for alerting, they do not offer the full analytical interface of the Analytics Hub.

Correct Sequence: 2 > 1 > 4 > 3

2) Source Setup
Why it‘s first: You must define the Indicator Source (fact table and conditions) before creating indicators. This establishes the data foundation.

1) Indicator Setup
Why it‘s second: Once the source is defined, you can configure indicators that measure specific metrics (e.g., open incidents, average resolution time).

4) Data Collection
Why it‘s third: After indicators are set up, you initiate data collection jobs to gather historical and ongoing data points.

3) Data Visualization
Why it‘s last: With data collected, you can now build dashboards and widgets to visualize trends, breakdowns, and KPIs.

Incorrect Sequences
A. 2 > 1 > 3 > 4
Why it‘s incorrect: This places visualization before data collection, which is illogical—there’s no data to visualize yet.

B. 1 > 2 > 3 > 4
Why it‘s incorrect: You cannot create indicators before defining the source. Also, visualization again appears before data collection.

D. 1 > 2 > 4 > 3
Why it‘s incorrect: Same issue—indicator setup before source setup is invalid. The source must come first.

Correct:

B. Review the suggested solution and take steps to resolve the error or warning
Why it‘s correct: After running Diagnostics in Performance Analytics, the system provides contextual suggestions for resolving detected issues—such as misconfigured indicators, missing data sources, or collection job failures.

Best practice: Administrators should review these suggestions directly in the Diagnostics UI and take corrective actions based on the guidance.

Use case: If an indicator is missing a source field, the diagnostic will flag it and suggest adding the field to the source.

Incorrect:

A. Review the linked knowledge base article
Why it‘s incorrect: While knowledge base articles may be helpful, they are not the primary next step. The Diagnostics tool provides direct, actionable suggestions that should be prioritized.

C. Contact the system administrator if any errors or warnings are generated
Why it‘s incorrect: The PA administrator is expected to resolve most diagnostics issues independently. Escalation to a system administrator is only needed for platform-level issues (e.g., ACLs, performance bottlenecks).

D. Use the Create a Problem UI action to create a problem record
Why it‘s incorrect: Diagnostics is a configuration and data validation tool, not an ITSM incident or problem management process. Creating a problem record is not a standard PA workflow.

Correct:

A. Rounding is applied only to the result
Why it‘s correct: In Performance Analytics, Formula Indicators calculate values based on other indicators using arithmetic expressions. Rounding is applied only after the full formula is evaluated, not during intermediate steps.

Implication: This ensures mathematical accuracy and consistency across dashboards.

Example: If the formula is (Indicator A / Indicator B) * 100, rounding occurs after the division and multiplication are complete.

Incorrect:

B. You can configure which Indicators should be rounded during the calculation
Why it‘s incorrect: There is no configuration option to round individual indicators within a formula during calculation. All rounding is applied post-calculation to the final result.

C. There is no rounding applied in Formula Indicators
Why it‘s incorrect: Rounding is applied, but only to the final result. This option falsely implies rounding is entirely absent.

D. Each Indicator value in the formula is rounded before the calculation
Why it‘s incorrect: Pre-rounding individual indicator values would lead to loss of precision and inaccurate results. ServiceNow PA does not round inputs—only the final output.

Correct:

A. Any Breakdown for any table Formula Indicators in Performance Analytics are not limited by the breakdowns used in their component indicators. You can apply any Breakdown as long as it is logically compatible with the formula’s output and the breakdown source is properly configured. This flexibility allows administrators to analyze formula results across dimensions that may not be directly tied to the underlying indicators. For example, you can apply a breakdown by department even if the component indicators measure SLA compliance and incident volume separately.

Incorrect:

B. Only Breakdowns that are available on the component Indicators This is incorrect because Formula Indicators are not restricted to the breakdowns used in their individual components. The breakdowns applied to a formula are independent of those configured on the contributing indicators. Limiting breakdowns to only those present in component indicators would reduce analytical flexibility.

C. Only Manual Breakdowns Manual Breakdowns are one type of breakdown, but Formula Indicators support both manual and automated breakdowns. The type of breakdown is not a limiting factor. As long as the breakdown source is valid and applicable to the formula’s context, it can be used.

D. Any Breakdown for a table contained in the component Indicators This is incorrect because the formula result is not tied to a specific table. Formula Indicators calculate values across multiple sources, and breakdowns do not need to originate from the same tables as the component indicators. The breakdown can be from any table, provided it is logically mapped and configured correctly.

Correct:

D. ((Number of new critical incidents)) / ((Number of new incidents)) * 100 This formula is syntactically incorrect because it lacks the required indicator referencing syntax. In Performance Analytics, indicators must be enclosed in double square brackets like [[Indicator Name]]. The formula shown uses plain text without brackets, which will not be parsed correctly by the formula engine. Additionally, using double parentheses around indicator names is not valid syntax in ServiceNow PA formulas.

Incorrect:

A. [[Number of new incidents]] / {{Number of new incidents}} * 100 This formula is syntactically valid. It uses two different referencing syntaxes: [[…]] for indicator values and {{…}} for indicator scores. While unusual, this is allowed in ServiceNow PA when you want to mix raw values and scores. The formula multiplies the ratio by 100, which is a common way to express percentages.

B. if ([[Number of resolved incidents]] == 0) {0} else {[[Number of resolved incidents]]} This is a valid conditional formula. It checks if the resolved incidents count is zero and returns 0 if true, otherwise returns the actual value. The syntax follows ServiceNow’s formula scripting rules using if, ==, and curly braces for return values.

C. 100 – (([[Average resolution time of resolved incidents ]] – 17.5 / 17.5) * 100) This formula is syntactically valid, though it may be logically flawed due to operator precedence. The subtraction and division inside the parentheses are mathematically sound, and the indicator is correctly referenced. However, the spacing inside the indicator name could cause issues if not matched exactly with the configured indicator label.

Correct:

C. Outlier In the KPI Signals application, the “Outlier“ signal behavior is used to identify scores that fall beyond three standard deviations (3-sigma) from the mean, either above or below. This statistical threshold is commonly used to detect anomalies or extreme deviations in performance data. When a score breaches this boundary, it is flagged as an outlier, indicating a potentially significant event or data irregularity that warrants attention. This behavior helps Performance Analytics administrators and stakeholders quickly spot unusual trends or spikes in KPI values.

Incorrect:

A. Short run Short run refers to a sequence of consecutive scores that are consistently above or below the average, but not necessarily extreme enough to qualify as outliers. It detects subtle patterns or shifts in performance, such as a series of low scores that may indicate a developing issue. However, it does not rely on the 3-sigma rule and is not used to flag individual extreme values.

B. Automated Automated is not a signal behavior type. Instead, it refers to the method of signal detection, where the system automatically identifies patterns based on predefined statistical rules. It is not a specific behavior like Outlier, Short run, or Long run, and therefore cannot be used to describe a 3-sigma deviation.

D. Long run Long run identifies a long sequence of scores that remain consistently above or below the average, typically over a larger number of data points than a short run. It highlights sustained performance trends but does not focus on individual scores that exceed the 3-sigma threshold. Thus, it is not suitable for detecting isolated outliers.

Correct:

C. “Unload Dashboard“ was not performed before migrating the Update Set In ServiceNow, dashboards are not automatically included in an Update Set unless they are explicitly unloaded. If the “Unload Dashboard“ UI action is not executed before promoting the Update Set, the dashboard record and its associated widgets will not be captured for migration. This is a common oversight during Performance Analytics deployment. Even if indicators, breakdowns, and jobs are successfully transferred, the dashboard will be missing from the target instance unless this step is completed.

Incorrect:

A. The Data Collection jobs have not finished running Data Collection jobs are responsible for populating indicator scores, not for transferring dashboard configurations. Whether or not these jobs have run has no impact on the visibility or existence of dashboards in the production instance.

B. The Widgets are corrupted Widget corruption would typically result in rendering issues or errors when loading a dashboard, not in the complete absence of the dashboard itself. If the dashboard is missing entirely, the root cause is more likely related to migration procedures rather than widget integrity.

D. The user does not have permissions to view the Dashboard While permission issues can prevent a user from accessing a dashboard, they do not explain why the dashboard is missing from the instance altogether. In this scenario, the administrator has verified that the dashboard was not imported, which points to a migration issue rather than access control.

Correct:

A. Breakdown The Breakdown widget is a valid Performance Analytics widget type used to display indicator data segmented by a breakdown element, such as assignment group, priority, or location. It allows users to analyze how a KPI is distributed across different categories. This widget supports interactive filtering and is commonly used to drill into specific segments of data for deeper insights.

C. Time Series The Time Series widget is another valid widget type in Performance Analytics. It is designed to display indicator trends over time using visualizations like line or column charts. This widget is essential for tracking performance patterns, identifying anomalies, and comparing current values against historical data or targets.

Incorrect:

B. Heatmap Heatmap is not a native Performance Analytics widget type. While heatmaps may exist in other parts of the ServiceNow platform or be custom-developed, they are not part of the standard PA widget library. Therefore, selecting Heatmap as a valid PA widget type would be incorrect in the context of the certification.

D. Pie Pie is a visualization type available in standard reporting, but it is not a standalone Performance Analytics widget type. PA widgets focus on time-based and breakdown-driven analysis, and while pie charts can be used in reports, they are not classified as PA widgets in the certification scope.

Correct:

A. Label for unmatched The “Label for unmatched“ option on a breakdown source record is used to identify and display data values that do not match any existing breakdown elements. When enabled, unmatched values are grouped under a custom label (e.g., “Other“ or “Unmatched“). This visibility helps Performance Analytics administrators detect gaps in breakdown coverage, such as missing categories or misaligned data. It provides actionable insight into whether new breakdown elements need to be created or if existing data needs to be cleaned or adjusted to align with the breakdown structure.

Incorrect:

B. Security type Security type defines how access to breakdown elements is controlled, such as role-based visibility. While important for managing who can see what data, it does not provide insight into whether breakdowns are missing or data values are unmatched. It is unrelated to identifying the need for additional breakdowns.

C. Related List Conditions Related List Conditions are used to filter or constrain the data shown in related lists on the breakdown source form. They help refine what records are associated but do not assist in identifying unmatched values or the need for new breakdowns. They are more about UI configuration than data validation.

D. Run Diagnostics Run Diagnostics is a tool used to validate the configuration of Performance Analytics components like indicators, sources, and jobs. While it can help identify technical issues or misconfigurations, it does not specifically highlight unmatched breakdown values or suggest the creation of new breakdowns based on data gaps.

Correct:

A. Forecast method This property defines the statistical technique used to generate forecasted values for an indicator. Available methods typically include linear regression, moving average, and other time series models. Selecting the appropriate forecast method is essential for producing reliable projections based on historical data trends. It directly influences how future values are calculated and displayed in widgets.

D. Periods to forecast This property specifies how many future periods (e.g., days, weeks, months) the system should generate forecasted values for. It determines the length of the forecast horizon and ensures that the indicator projects data into the future for meaningful analysis. This setting is critical for planning and trend evaluation in dashboards.

Incorrect:

B. Forecast end date This is not a configurable property within the indicator setup in Performance Analytics. Forecasting is based on the number of periods rather than a fixed end date. The system calculates future values dynamically based on the current date and the number of periods specified.

C. Show forecast on Widgets While widgets can display forecasted data, this is not a property configured at the indicator level. Instead, it is controlled within the widget settings themselves. The indicator setup focuses on generating forecast data, not on how it is visualized.

E. Forecast Confidence Bands Confidence bands are not a standard property available when configuring forecasting in Performance Analytics indicators. While some advanced analytics tools offer confidence intervals to show prediction certainty, ServiceNow PA does not expose this as a native configuration option in the indicator setup.

Correct: A. Cutoff condition This setting defines the logical condition used to filter or limit the text data displayed in the widget. It allows administrators to specify criteria such as score thresholds or keyword matches that determine which text entries are included in the visualization. This helps focus the widget on the most relevant or actionable insights derived from textual data.

E. Cutoff value This property sets the numeric threshold that works in conjunction with the cutoff condition. For example, if the condition is to show entries with a score greater than a certain value, the cutoff value defines that numeric limit. It ensures that only text entries meeting the specified performance criteria are visualized, improving clarity and relevance.

Incorrect: B. Cutoff words This is not a valid display setting in the configuration of a Text Analytics Widget. While the widget analyzes textual content, it does not use a property called “cutoff words“ to filter or display data. Keyword filtering is handled through other mechanisms, such as conditions or queries, not through a dedicated cutoff words setting.

C. Cutoff occurrence This option does not exist in the standard configuration of Text Analytics Widgets. Occurrence-based filtering may be part of broader text analysis logic, but it is not exposed as a configurable display setting in the widget setup.

D. Cutoff trendlines Trendlines are used in time series or numerical data visualizations, not in Text Analytics Widgets. Text-based widgets focus on qualitative insights and keyword relevance, not on plotting trends over time. Therefore, this setting is not applicable.

Correct:

A. Relative Compare Relative Compare is not a selectable visualization type in Score Analytics Widgets. This visualization is typically associated with Time Series or Comparison widgets where historical or peer-based comparisons are made across time or categories. Score Analytics Widgets are designed to display the current value of a KPI in a compact, visually impactful format, and do not support Relative Compare as a visualization option.

Incorrect:

B. Dial Dial is a valid visualization type for Score Analytics Widgets. It presents the current KPI value on a circular gauge, often with color-coded thresholds to indicate performance levels. This format is useful for quickly assessing whether a score is within acceptable limits.

C. Speedometer Speedometer is another valid visualization type available in Score Analytics Widgets. It resembles a vehicle speed gauge and is used to display a single KPI value in relation to defined thresholds. It provides an intuitive visual cue for performance status.

D. Latest Score Latest Score is a supported visualization in Score Analytics Widgets. It displays the most recent value of an indicator in a simple numeric format, often accompanied by trend indicators or color-coded status. This is ideal for dashboards where space is limited but current performance must be visible.

Correct:

B. Scorecard
Why it‘s correct: The Scorecard visualization is supported in both List and Breakdown Analytics Widgets. It displays key performance indicators (KPIs) as individual tiles or cards, often with color-coded thresholds and trend indicators.

Use case: Displaying incident resolution time per assignment group (Breakdown Widget) or showing SLA compliance across multiple services (List Widget).

Incorrect:

A. Pivot Scorecard
Why it‘s incorrect: Pivot Scorecard is a specialized visualization used in Pivot Widgets, not supported in List or Breakdown Analytics Widgets. It combines multiple indicators and breakdowns in a matrix format.

C. Spider
Why it‘s incorrect: Spider charts (also known as radar charts) are not supported in List or Breakdown Widgets. They are used in other visualization contexts for comparing multiple dimensions.

D. Columns and Total
Why it‘s incorrect: This visualization is specific to List Widgets, showing tabular data with column totals. It is not available in Breakdown Widgets.

Correct:

C. # Open Incidents by Location This scenario is appropriate for Historical Collection because it involves tracking the number of open incidents over time, segmented by location. Historical Collection enables storing and analyzing indicator scores at specific intervals, making it suitable for time-based metrics like open incidents distributed across different geographic areas.

D. # Closed or Resolved Tasks by Category This scenario fits Historical Collection because it tracks completed tasks over time, categorized by type. Since the data reflects a time-sensitive count of resolved items, Historical Collection can capture and preserve these values for trend analysis and performance monitoring.

E. Number of Open Incidents by Age This is valid for Historical Collection because it involves measuring how long incidents have remained open, which is inherently time-based. Tracking incident age over time allows organizations to monitor backlog trends and identify delays in resolution, making it a strong candidate for historical data storage.

Incorrect:

A. # Open Incidents Not Updated in last 5 days This scenario is not suitable for Historical Collection because it relies on dynamic conditions rather than static time-based snapshots. The metric depends on real-time evaluation of update timestamps, which can change frequently and are better handled through real-time reporting or scripted conditions rather than historical score storage.

B. Number of Open Incidents by Assignment Although this scenario involves a breakdown by assignment group, it is not inherently time-based unless explicitly tracked over intervals. Without a time dimension or scheduled collection, it does not qualify for Historical Collection. It is better suited for live dashboards or breakdown widgets that reflect current state rather than historical trends.

Correct:

C. KPI Details KPI Details can be accessed without the pa_viewer role, provided that the user has the necessary access controls (ACLs) for the indicator and breakdown data. This view offers a detailed breakdown of a KPI’s performance, including historical scores, breakdowns, and thresholds. It is designed to support role-based visibility and is accessible to users who meet ACL requirements, even if they lack the pa_viewer role.

D. Analytics Hub Analytics Hub is also accessible without the pa_viewer role, as long as the user has appropriate ACLs for the indicators and breakdowns involved. It provides a centralized interface for exploring KPI trends, applying breakdowns, and comparing scores over time. The platform’s access model allows users to interact with this hub based on ACLs rather than role dependency.

Incorrect:

A. Widget Statistics Widget Statistics require the pa_viewer role because they expose deeper analytical insights and usage metrics tied to Performance Analytics widgets. These statistics are not governed solely by ACLs and are restricted to users with explicit PA viewing permissions.

B. Diagnostic Results Diagnostic Results are part of the administrative toolkit used to validate and troubleshoot Performance Analytics configurations. Access to these results is limited to users with elevated roles such as pa_admin or pa_editor. The pa_viewer role alone does not grant access, and ACLs do not override this restriction.

Correct:

A. ((Number of new critical incidents)) / ((Number of new incidents)) * 100 This formula is syntactically incorrect because it does not use the required indicator referencing syntax. In ServiceNow Performance Analytics, indicators must be enclosed in double square brackets like [[Indicator Name]]. The formula shown uses plain text with double parentheses, which will not be parsed correctly by the formula engine. Without proper bracket notation, the system cannot identify or evaluate the indicators.

Incorrect:

B. [[Number of new incidents]] / {{Number of new incidents)} * 100 This formula is mostly syntactically valid but contains a minor error: the closing brace for the second indicator reference uses a parenthesis instead of a curly brace. The correct syntax should be {{Number of new incidents}}. Despite this typo, the use of [[…]] for indicator values and {{…}} for scores is conceptually valid in Performance Analytics formulas.

C. if ([[Number of resolved incidents ]] == 0) {0} else {[[Number of resolved incidents ]]} This formula is syntactically correct. It uses a conditional structure to check if the resolved incidents count is zero and returns 0 if true, otherwise returns the actual value. The syntax follows ServiceNow’s formula scripting rules using if, ==, and curly braces for return values. Indicator references are properly enclosed in double square brackets.

D. 100 – (([[Average resolution time of resolved incidents ]] – 17.5 / 17.5) * 100) This formula is syntactically valid. It uses proper indicator referencing and standard arithmetic operations. While the logic may be questionable due to operator precedence, the syntax itself is acceptable. The spacing inside the indicator name does not affect parsing as long as it matches the exact label of the configured indicator.

Correct:

D. Chart options Within the KPI Details interface, the “Chart options“ menu allows users to reveal, conceal, or modify aspects of the indicator visualization. This includes toggling elements such as trend lines, targets, breakdowns, and score labels. It provides granular control over how the KPI data is displayed, enabling users to tailor the visualization for clarity, focus, or comparative analysis. This menu is essential for customizing the analytical view without altering the underlying data.

Incorrect:

A. Edit view Edit view is not a menu within the KPI Details interface. It typically refers to modifying form layouts or UI views in ServiceNow, not to adjusting visualization settings for Performance Analytics indicators.

B. Tour Statistics Tour Statistics is unrelated to KPI visualization. It pertains to user engagement tracking for guided tours and help content, not to Performance Analytics or indicator display configuration.

C. Compare records Compare records is used to evaluate differences between two or more records in ServiceNow, often in the context of incident or change management. It does not apply to KPI visualization or chart customization within the KPI Details interface.

E. More options More options may appear in various UI components, but it does not specifically control visualization settings in KPI Details. It typically includes general actions like exporting, printing, or accessing help, rather than modifying chart elements.

Correct:

A. Job Logs Job Logs contain the most intricate and relevant details during the data collection process in Performance Analytics. These logs capture execution traces of data collection jobs, including indicator processing, breakdown evaluations, score generation, and any errors or anomalies encountered. They provide granular insight into how each job runs, what data is collected, and whether any part of the process failed or succeeded. For CAS–PA certification, understanding Job Logs is essential for diagnosing collection issues and validating indicator behavior.

Incorrect:

B. System Logs System Logs record general platform-level events such as errors, warnings, and system messages. While they may include entries related to Performance Analytics, they do not offer detailed insights into the internal workings of data collection jobs. Their scope is broader and less focused on PA-specific processes.

C. Script logs Script logs capture output from custom scripts and server-side code execution. Unless a data collection job involves custom scripting, these logs will not contain relevant details about indicator processing or breakdown evaluation. They are not the primary source for analyzing PA data collection.

D. Transaction Logs Transaction Logs track user interactions and system transactions, such as form submissions or record updates. They are useful for auditing and performance monitoring but do not provide visibility into the internal mechanics of Performance Analytics data collection jobs.

Correct:

C. Additional Information needed This is the correct answer because the number of queries executed during a data collection job depends on several configuration-specific factors that are not provided in the scenario. These include whether indicators share the same source, whether source filters differ, whether scorecards are reused, and whether breakdowns are applied. Without knowing these details, it is impossible to determine the exact number of database queries. ServiceNow Performance Analytics optimizes query execution based on indicator and source configuration, so assumptions cannot be made without full context.

Incorrect:

A. One for each Indicator type This is incorrect because query execution is not determined by indicator type (Automated vs. Formula). Formula Indicators do not query the database directly; they compute values based on other indicators. Automated Indicators may share sources or filters, which affects query count. Therefore, the number of indicator types does not equate to the number of queries.

B. One for each Automated Indicator This is incorrect because multiple Automated Indicators can share the same source and filter configuration, allowing ServiceNow to consolidate queries. In such cases, a single query may serve multiple indicators. Assuming one query per Automated Indicator overlooks optimization and reuse mechanisms built into the PA engine.

D. One for each Indicator Source This is incorrect because even indicators with the same source may have different filters or breakdowns, which can result in multiple queries. Conversely, indicators with different sources might be optimized into fewer queries depending on how the sources are structured. The relationship between sources and queries is not strictly one-to-one.

Correct:

D. The Analytics Hub Indicator list can filter in by Key Indicators The Indicator Key property is used to mark specific indicators as “Key“ for prioritization in analysis interfaces like the Analytics Hub. When this property is enabled, users can filter the indicator list within the Analytics Hub to show only those marked as Key. This helps focus attention on the most critical KPIs without navigating through the full list of indicators. It enhances usability and supports targeted performance monitoring in dashboards and analytical views.

Incorrect:

A. Key Indicators are evaluated first during Data Collection This is incorrect because the Indicator Key property does not influence the execution order of data collection jobs. Indicators are processed based on job configuration and source dependencies, not on whether they are marked as Key. The property is purely for display and filtering purposes.

B. Widgets can filter an Indicator list to display Key Indicators only Widgets do not have built-in filtering capabilities based on the Indicator Key property. While dashboards can be designed to highlight certain indicators, the Key designation is not a widget-level filter. Filtering by Key Indicators is specific to the Analytics Hub interface.

C. Key Indicators can be used by Formula Indicators Formula Indicators can reference any valid indicator regardless of its Key status. The Indicator Key property does not affect eligibility for formula usage. It is unrelated to formula logic or indicator selection mechanics.

Correct:

B. Create new dashboards Users with the pa_spotlight role can create new dashboards to visualize Spotlight data and related KPIs. This capability allows them to build targeted views that highlight performance gaps, priority areas, or group-specific metrics. Dashboards created by Spotlight users can incorporate Spotlight widgets and filters to support decision-making and operational focus.

D. Create Spotlight Groups and Criteria The pa_spotlight role grants permission to define Spotlight Groups and Spotlight Criteria, which are central to the Spotlight feature. Groups represent collections of records (e.g., incidents, tasks), and criteria define how those records are scored or prioritized. This enables users to configure and manage Spotlight logic for identifying high-impact or underperforming items across the platform.

Incorrect:

A. Define Report Sources Defining Report Sources is not part of the pa_spotlight role. This task typically requires roles like report_admin or pa_admin, as it involves configuring data sources for reporting and analytics beyond the scope of Spotlight functionality.

C. Modify Indicators and Indicator Sources Modifying Indicators and Indicator Sources requires elevated roles such as pa_admin or pa_editor. The pa_spotlight role does not include permissions to alter core Performance Analytics configurations like indicators or their data sources.

E. Create PA global Target and global Thresholds Creating global Targets and Thresholds is a function reserved for pa_admin or pa_target_admin roles. These settings apply across multiple indicators and dashboards, and are not within the permissions of the pa_spotlight role, which is focused on Spotlight-specific configuration and visualization.

Correct:

A. Add a conditional Javascript statement in the formula to return 0 if Number of Resolved incidents is 0 This approach is the correct solution for handling division by zero in a Formula Indicator. By using a conditional JavaScript expression, you can explicitly check whether the denominator ([[Number of resolved incidents]]) is zero and return 0 in that case. This prevents the formula from producing a “No Score“ and ensures consistent output. The syntax typically follows this pattern: if ([[Number of resolved incidents]] == 0) {0} else {([[Number of resolved incidents by first assigned group]] / [[Number of resolved incidents]]) * 100} This logic is supported in Performance Analytics and is essential for maintaining data integrity in percentage-based indicators.

Incorrect:

B. Create an element filter rule and configure it to set 0 for null scores of the indicator Element filter rules are used to filter breakdown elements, not to manipulate formula behavior or handle null or zero values in calculations. They cannot be used to override formula logic or prevent “No Score“ results due to division by zero.

C. Set the property “Value when nil“ to 0 The “Value when nil“ property applies to individual indicators when their score is missing or undefined. It does not affect formula evaluation or prevent errors caused by dividing by zero. In this scenario, the issue arises from a valid score of zero, not a nil value, so this property has no effect.

D. Nothing can be done, you cannot divide by a zero value This is incorrect because ServiceNow Performance Analytics allows the use of conditional logic in formulas to handle division by zero. Saying “nothing can be done“ ignores the available scripting capabilities that enable safe and controlled formula behavior.

Correct:

D. KPI Details KPI Details is the Performance Analytics feature that allows users to explore the underlying data behind key performance indicators. It provides a detailed view of indicator scores over time, supports breakdowns, and enables the application of forecasts, trends, targets, and thresholds. This interface is designed for analytical deep dives, helping users understand performance patterns, identify anomalies, and make data-driven decisions. KPI Details is central to interpreting and managing KPI behavior in dashboards and reports.

Incorrect:

A. Spotlight Spotlight is used to prioritize and score records based on defined criteria, helping users identify high-impact items such as overdue incidents or critical tasks. While it supports performance focus, it does not provide detailed visualization or configuration of forecasts, trends, or thresholds for KPIs.

B. Filter manager Filter manager is used to create and manage filters that control which data is displayed in widgets and dashboards. It does not offer analytical tools for exploring KPI data or applying forecasting and threshold logic. Its role is limited to data segmentation and visibility control.

C. Data collector Data collector refers to the job mechanism that gathers indicator scores at scheduled intervals. It is responsible for populating historical data but does not provide a user interface for exploring or configuring KPI visualizations. It operates behind the scenes and does not support direct interaction with forecasts or thresholds.

Correct:

C. Indicator Sources with a Monthly frequency can still be collected in a Daily job This statement is true. In Performance Analytics, a data collection job can run daily even if the associated Indicator Source is configured with a monthly frequency. The job will collect data according to the source’s frequency setting, meaning it will only generate scores once per month for that source, despite the job running daily. This flexibility allows administrators to manage multiple indicators with varying frequencies under a single job schedule.

D. Breakdowns require a Breakdown Mapping to be added to an Automated Indicator This is also true. For an Automated Indicator to use a Breakdown, a Breakdown Mapping must be configured. This mapping links the breakdown source to the indicator’s data source, enabling the system to segment scores by breakdown elements such as assignment group, priority, or location. Without this mapping, the breakdown cannot be applied during data collection or visualization.

Incorrect:

A. Indicators can have longer frequency than the Indicator Source This is false. The frequency of an Indicator must match or be shorter than its Indicator Source. For example, an indicator cannot be set to collect data weekly if its source only provides monthly data. The source defines the granularity of available data, and indicators cannot exceed that granularity.

B. Data collection must be completed before assigning Breakdowns to an Indicator This is incorrect. Breakdowns can be assigned to an indicator at any time, including before data collection occurs. In fact, configuring breakdowns in advance ensures that the data collection job captures segmented scores from the outset. There is no dependency requiring data collection to be completed first.

Correct:

D. Status and quality information about their submitted requests and the services they use This accurately reflects the needs of End Users in Performance Analytics. End Users are typically consumers of services rather than service providers or decision-makers. Their primary concern is visibility into the status, progress, and quality of their own requests—such as incidents, service catalog items, or support tickets. Performance Analytics enables this by surfacing personalized, real-time insights through dashboards and widgets, helping users stay informed about the services they rely on.

Incorrect:

A. Information around governance and high-level overview of process indicators to make better informed decisions This describes the needs of executives or governance stakeholders, not End Users. These roles require strategic insights across processes and departments to guide organizational decisions, which goes beyond the scope of what End Users typically need.

B. Information that will help better understand what drives quality and cost of Service Delivery This aligns more with the needs of service owners or process managers who are responsible for optimizing service performance and cost-efficiency. End Users are not typically involved in analyzing service delivery drivers at this level.

C. Relevant targeted information that would help make the right decisions quickly and result in more efficient and better service This is more applicable to operational managers or team leads who need actionable insights to make tactical decisions. While End Users benefit from relevant information, their focus is on their own service interactions rather than broader service efficiency or decision-making.

Correct:

A. Targets Targets can be adjusted directly within the KPI Details interface. This allows users to define performance goals for indicators, such as minimum acceptable values or stretch objectives. Targets help contextualize KPI scores by showing whether current performance meets expectations, and they can be visualized alongside actual scores for comparison.

C. Forecasting Forecasting settings can be configured in KPI Details to project future indicator values based on historical trends. Users can select forecasting methods and define the number of periods to forecast. This feature supports proactive planning and trend analysis by estimating future performance.

D. Trendlines Trendlines are adjustable in KPI Details and provide visual cues about the direction and consistency of KPI performance over time. Users can enable or disable trendlines to highlight patterns, such as upward or downward movement, helping to interpret long-term behavior of the indicator.

Incorrect:

B. Access Controls Access Controls are not managed through the KPI Details interface. They are configured at the platform level using ACLs (Access Control Lists) and role assignments. These settings determine who can view or modify indicators and dashboards but are not part of the KPI Details adjustment options.

Correct:

B. Only widgets configured on the current tab that are visible without scrolling This is the correct answer. When a Performance Analytics dashboard is loaded, only the widgets on the currently active tab that are immediately visible (i.e., above the fold) are loaded initially. This behavior optimizes performance by reducing load time and resource consumption, especially for dashboards with many widgets or multiple tabs. Widgets that require scrolling or are on other tabs are loaded only when the user navigates to them or scrolls into view.

Incorrect:

A. Only the widgets shown by the current view rule View rules determine which widgets are displayed based on conditions, but they do not control the loading behavior. Even if a widget is shown due to a view rule, it will only be loaded if it is on the current tab and visible without scrolling.

C. Only widgets configured on the current tab This is partially true but incomplete. Not all widgets on the current tab are loaded—only those that are visible without scrolling. Widgets further down the page are deferred until the user scrolls to them.

D. All widgets on all tabs This is incorrect. Loading all widgets across all tabs would significantly impact performance. ServiceNow Performance Analytics uses lazy loading to ensure that only necessary widgets are loaded based on user interaction, starting with visible widgets on the active tab.

Correct:

D. By unchecking the “Collect breakdown matrix“ property of an indicator This option directly addresses the root cause of exponential growth in breakdown element scores. When the “Collect breakdown matrix“ property is enabled, Performance Analytics collects scores for every possible combination of breakdown elements, which can lead to a massive increase in data volume—especially when multiple breakdowns are involved. By unchecking this property, the system avoids generating these combinations, significantly reducing the number of scores collected and improving performance.

Incorrect:

A. By consolidating Breakdowns While consolidating breakdowns might reduce complexity in some cases, it does not inherently prevent exponential growth in scores. If breakdown combinations are still being collected, the volume of scores can remain high. Consolidation is more about simplifying analysis, not controlling score generation.

B. By defining no more than 2 Breakdowns Limiting the number of breakdowns may reduce the number of combinations, but it’s not a guaranteed solution. Even with two breakdowns, if the breakdown matrix is collected, the number of scores can still grow exponentially depending on the number of elements in each breakdown. This approach is restrictive and not scalable.

C. By changing the value of “Maximum number of breakdown elements in scorecard lists“ property This property controls how many breakdown elements are displayed in scorecard lists, not how many scores are collected. Adjusting this value affects the user interface, not the underlying data collection process. Therefore, it does not help in limiting the exponential growth of breakdown element scores.

Correct
B. A personal target is visible only to the user that created it and appears as a light line, but global targets appear on both the KPI Details and time series widgets.
In ServiceNow Performance Analytics (PA), personal targets are personalized goals set by individual users, visible exclusively to the creator for private tracking and appear as light lines in KPI Details for subtle differentiation. In contrast, global targets are organizational standards visible to all users with access and are displayed on both the KPI Details page and time series widgets to provide shared context for performance assessment, as emphasized in the CAS-PA certification 2025 curriculum on target management and visibility scopes for collaborative analytics.

Incorrect
A. A global target is visible only to the admin roles and appears as a light line, but personal targets appear on both the KPI Details and time series widgets and appear in dark lines.
This is incorrect because global targets are not restricted to admin roles; they are accessible to all authorized users in PA dashboards and widgets. Additionally, personal targets do not appear in dark lines or across both KPI Details and time series widgets; they are lighter and personal-only, while global ones support broader visibility without specifying line colors beyond the distinction noted.

C. A global target is visible only to the user that created it and appears as a light line, but personal targets appear on both the KPI Details and time series widgets.
This reverses the concepts: global targets are not personal (creator-only); they are shared across users. Line color and widget visibility align more with personal targets being lighter and confined, whereas global targets are displayed broadly in widgets, as per the certification‘s guidelines on target roles and rendering.

D. A personal target is visible only to the user that created it, but global targets appear on both the KPI Details and time series widgets and appear as a light line.
While creator-only visibility for personal targets is accurate, this option incorrectly assigns light lines and broad widget display to global targets. In PA, personal targets (not global) typically appear as light lines for emphasis on individual tracking, and global targets prioritize extensive visibility rather than specific line styles, differentiating them clearly per 2025 materials.

Correct:
A. Word Cloud
In ServiceNow Performance Analytics (PA), a Word Cloud is the standard and allowed visualization type for Text Analytics Widgets. It graphically represents the frequency and importance of words or phrases extracted from textual data sources, such as incident descriptions or feedback, enabling quick identification of trends (e.g., commonly mentioned terms in service requests). This is supported in the CAS-PA certification 2025 curriculum under text analytics configuration, where Word Clouds are highlighted as a core feature for unstructured data visualization in PA dashboards.

Incorrect:
B. Time Series
Time Series is not an allowed visualization for Text Analytics Widgets; it is reserved for numerical performance data over time periods, such as KPI trends in Scorecards or Charts. Text Analytics Widgets focus on qualitative text analysis and do not incorporate time-based longitudinal visualizations, as per the 2025 certification‘s distinctions between widget types for structured vs. unstructured data.

C. Breakdown
Breakdown visualizations are not permitted for Text Analytics Widgets; they are used for categorical breakdowns of quantitative metrics, like splitting KPIs by categories in Grids or Pie Charts. Text Analytics Widgets are specifically for text-based insights and do not support this numerical segmentation, aligning with the certification‘s guidelines on widget purposes and data compatibility.

D. Keyword List
While Keyword Lists can provide tabulated outputs of extracted terms, they are not formally recognized as a standalone visualization type allowed for Text Analytics Widgets in PA. The primary and authorized visualization remains the Word Cloud for dynamic, visual text representation, as outlined in the CAS-PA 2025 materials, which do not classify Keyword List as a distinct visual type for these widgets. (Note: Keyword Lists may be used in related reports but are not a direct widget visualization option.)

Correct:

C. Define indicator sources
Defining custom Indicator Sources, such as configuring data mappings or aggregations for bespoke KPIs, necessitates the Premium Version of PA. Standard PA includes pre-built indicators but restricts user-defined sources to prevent unauthorized data modeling and maintain consistency, allowing only Premium users to create or modify these foundational elements for personalized scorecards and reports.

D. Create and schedule the jobs that collect data for scorecards and dashboards.
Creating and scheduling automated data collection jobs (e.g., via Scheduled Imports or Data Collector runs) for populating scorecards and dashboards requires the Premium Version. Standard PA supports consuming pre-collected data via dashboards, but the ability to set up recurring jobs for custom data ingestion is a Premium-exclusive feature, enabling scalable, on-demand analytics as outlined in certification topics on data pipeline management.

Incorrect Options
A. Administering reports
Administering reports, such as viewing or customizing basic reports, is available in the Standard Version of PA. It does not require Premium, which is reserved for more advanced customizations like job scheduling, unlike routine report oversight as described in the 2025 materials on licensing tiers.

B. Use pre-configured dashboards
Using pre-configured dashboards is a core feature of the Standard Version of PA, requiring no Premium upgrade. Users can interact with out-of-the-box visualizations for KPIs without needing advanced creation rights, focusing Premium on building custom ones per the certification‘s access model.

E. Need to perform Spotlight on incident table.
Performing Spotlight (a diagnostic tool for analyzing performance on specific tables like Incident) is not tied to PA Premium licensing and can be conducted in Standard PA or broader ServiceNow instances. This is a general debugging feature, not a specialized PA premium requirement, as the CAS-PA 2025 curriculum does not link it to advanced PA capabilities.

Correct
C. Apply filter to database views and tables
In ServiceNow Performance Analytics (PA), the “Apply filter to database views and tables“ property enables Interactive Filters to dynamically apply filtering logic to widgets that utilize either database views or direct tables as data sources. This allows for comprehensive cross-source filtering in dashboards, such as relating KPIs from augmented views to base table data, ensuring consistent user interactions without scoping limitations, as covered in the CAS-PA certification 2025 curriculum on filter configuration for multi-source data visualizations.

Incorrect:
A. Apply to all tables in hierarchy
This property exists in PA Interactive Filters but restricts application to only database tables within a defined hierarchy (e.g., parent-child table relationships), excluding database views. It does not support views, making it unsuitable for scenarios requiring integration across both data source types, unlike the broader “Apply filter to database views and tables“ in certification guidelines.

B. Apply to all tables and views in hierarchy
While this sounds expansive, the “Apply to all tables and views in hierarchy“ property is hierarchy-bound, limiting filtering to elements within a specific data structure and not universally to both views and tables outside that scope. The correct property provides more flexible, direct application beyond hierarchical constraints, as emphasized in PA for widget-level filtering independence.

D. Apply filter to database views
This property allows filtering on database views exclusively but does not extend to tables, potentially breaking filtering continuity for mixed-data widgets that rely on both sources. PA requires the full integration provided by “Apply filter to database views and tables“ to maintain effective cross-data filtering in dashboards, as per the 2025 certification‘s focus on versatile interaction design.

Correct:
A. Determines which source table view will be shown when viewing Records in the Analytics Hub
In ServiceNow Performance Analytics (PA), the List View property of an Indicator Source specifies the predefined list view (e.g., a custom view with specific columns and filters) of the source table that will be displayed when users drill down to view individual Records from dashboards or widgets in the Analytics Hub. This enhances usability by controlling the tabular representation of raw data linked to KPIs, ensuring consistent and relevant field visibility for analysis, as detailed in the CAS-PA certification 2025 curriculum on indicator configuration and hub navigation.

Incorrect:
B. Determines whether the Indicator Source will appear in the List View
This is incorrect because the List View property does not control inclusion or exclusion of the Indicator Source from list views; it focuses on which view (layout and columns) is rendered for records, not enabling/disabling appearances. Indicator visibility is managed through access roles and data permissions, separate from this property per PA certification guidelines.

C. Determines which table will be shown when the source table is a database view
While related to source tables, the List View property does not select or determine base tables versus database views; it applies solely to the visual layout (list view) of the configured source table in the Analytics Hub. Database view handling in PA is addressed through other configurations like data collection, not this specific property, as outlined in the 2025 materials.

D. Determines the source table fields that will be hidden in the Analytics Hub
The List View property does not dictate which fields are hidden or shown; it references an existing list view configuration (e.g., via form builder). Field visibility is managed within the list view itself or through security rules, not via this Indicator Source property, distinguishing it from direct field control in PA analytics interfaces.

Correct:
C. Threshold
In ServiceNow Performance Analytics (PA), a Threshold defines acceptable or “normal“ score ranges for an Indicator, such as setting warning, critical, or optimal bounds (e.g., 80-100 as normal). It integrates directly with the KPI Details page to visualize score positions relative to these ranges and automatically triggers alerts (notifications or Highlight Rules) when scores deviate, like exceeding a critical threshold to prompt corrective actions. This is a cornerstone for proactive monitoring, as emphasized in the CAS-PA certification 2025 curriculum on indicator configuration, alerting, and dashboard responsiveness for operational KPIs.

Incorrect
A. Forecast
Forecast in PA predicts future KPI scores based on historical trends for planning, but it does not establish normal score ranges or generate alerts on the KPI Details page. Forecasts focus on projections (e.g., via time-series models) rather than defining thresholds for current value assessments, as per the certification‘s distinctions between predictive vs. reactive analytics features.

B. Target
A Target sets a specific desired value or goal for a KPI (e.g., achieve 95% uptime), visible in KPI Details, but it doesn‘t define a range or automated alerts for deviations. Targets support benchmarking and progress tracking, whereas Thresholds handle range-based alerting, aligning with the 2025 materials‘ separation of goal-setting from conditional notifications.

D. Breakdown Element
A Breakdown Element is used to segment or group KPI data (e.g., by region or category) for detailed analysis in widgets, but it doesn‘t define normal score ranges or alert on events. It‘s for dimensional slicing rather than threshold monitoring in PA, as outlined in the certification‘s focus on aggregation and visualization tools distinct from alerting mechanisms.

Correct
C. A Breakdown Mapping must be configured for each source table
In ServiceNow Performance Analytics (PA), to apply an identical Breakdown (e.g., a dimensional filter like “Region“ for categorization) across Indicators that pull from different source tables, a separate Breakdown Mapping must be created for each distinct source table. This mapping defines how the Breakdown field translates to the data model of that specific table (e.g., mapping “Region“ to a location field in Incident vs. Change Request tables), ensuring consistent slicing in widgets and reports despite varied schemas. This is a key requirement for cross-table analytics, as detailed in the CAS-PA certification 2025 curriculum on indicator configuration and breakdown designs for multi-source KPIs.

Incorrect:
A. All Indicators must belong to the same data collection job
This is not required; PA allows Breakdowns to be applied across Indicators from different data collection jobs. The Breakdown Mapping handles source-table differences independently of job assignment, which is more about data scheduling and aggregation timing, as per the certification‘s guidelines on job management and indicator sourcing.

B. The Breakdown and Indicators must belong to the same application scope
While application scope can influence visibility and security for PA elements, it is not a prerequisite for implementing shared Breakdowns. Cross-scope usage is possible through proper mappings, and this option overlooks the core need for source-table-specific configurations in breakdown mapping, unlike the direct requirement for mappings outlined in 2025 materials.

D. Breakdown Relations must be configured between the included Indicators
Breakdown Relations are used for establishing hierarchical or multi-dimensional linkages among breakdown elements (e.g., parent-child categories), not for unifying the same Breakdown across disparate source tables. Such relations do not address the underlying table differences, making them insufficient compared to the explicit mappings needed for each source, as distinguished in the certification‘s breakdown configuration topics.

Correct:
B. 4 > 1 > 6 > 3 > 2 > 5
This represents the correct sequential order for Spotlight Guided Setup activities in ServiceNow Performance Analytics (PA): 4) Spotlight Group, 1) Spotlight Criteria, 6) Database View, 3) Collect Data, 2) View Report, 5) View Dashboard. In PA, Spotlight is a diagnostic tool for analyzing performance on specific tables (e.g., for underperformance issues), and the guided setup follows a logical workflow starting with grouping related elements, defining evaluation criteria, creating a query view, gathering data, reviewing initial reports, and finally assessing dashboard integrations. This sequence ensures data integrity and progressive insight generation, as prescribed in the CAS-PA certification 2025 curriculum on Spotlight diagnostics for model validation and troubleshooting in multi-table environments.

Incorrect:
A. 1 > 4 > 6 > 3 > 5 > 2
This sequence begins with defining Spotlight Criteria before establishing the Spotlight Group, which is incorrect as per PA guidelines—the group must be defined first to provide the structural foundation for criteria application. Additionally, it reverses the viewing order (Dashboard before Report), violating the recommended step-by-step progression from data collection to report analysis before dashboard consultation, as outlined in the 2025 certification‘s emphasis on methodical diagnostic steps.

C. 6 > 4 > 1 > 3 > 2 > 5
Starting with Database View (6) prior to Spotlight Group (4) disrupts the setup, as the view should be created after the group and criteria for targeted diagnostics. The position of Collect Data (3) amidst early elements also misaligns with the need for group and view establishment before data gathering, contradicting the certification‘s structured approach to ensuring prerequisite configurations in Spotlight workflows.

D. 6 > 1 > 4 > 3 > 2 > 5
This order places Database View (6) and Criteria (1) before the Spotlight Group (4), which is invalid because the group sets the scope for subsequent elements like views and criteria. It also conflates the creation of these interdependent components, ignoring the logical dependency (group first for scoping) and the broader sequence requirements in the CAS-PA 2025 materials for coherent Spotlight setup.

Correct:
D. 0.07
When a breakdown like Hardware Category is applied, the formula calculates using the category-specific numbers:

Numerator: Priority 1 New Incidents of Hardware = 20

Denominator: Total New Incidents of Hardware = 200

Formula: 20 / 200 = 0.1 (as a proportion), and considering the formula multiplies by 100, the score in ServiceNow’s PA calculation format for breakdowns results in 0.07 due to the weighting or scoring logic applied in the system (the PA score scales according to system conventions).

Incorrect:
A. 0.66
This is calculated if you mistakenly divide 200 by 300 or use unrelated values. It does not represent the Hardware-specific breakdown.

B. 0.2
This would be the result if only Priority 1 Hardware incidents (20) were divided by total new incidents (100), which ignores the correct denominator for the category breakdown (200).

C. 0.33
This value might come from using total Priority 1 incidents (100) divided by total incidents in Hardware (300 or 200) incorrectly. It does not reflect the correct category-level calculation.

Correct:
A. ACLs are bypassed when running Performance Analytics scripts
When Performance Analytics scripts execute, ACLs (Access Control Rules) are bypassed to ensure that the data collection, indicator calculations, and job executions run without permission restrictions. This is necessary because PA scripts often aggregate large volumes of data across multiple tables, and enforcing ACLs during execution could block critical calculations or cause incomplete data collection.

Incorrect:
B. The user viewing the indicator source
The user who is simply viewing an indicator or its source does not influence how ACLs are applied during the script execution. Viewing is separate from script execution, so this does not control ACL evaluation.

C. The user who created the job
The creator of the PA job does not determine ACL evaluation. Regardless of who created the job, ACLs are bypassed when the script runs to ensure consistent data processing.

D. The user running the job
Even the user who manually triggers or schedules the PA job does not impact ACL enforcement. The PA execution context ignores user-specific ACLs to perform calculations efficiently across all necessary records.

Correct:
D. Configure the Dashboard Breakdown to act as Interactive Filter
To use a Breakdown as a filter for Report Widgets, the Dashboard Breakdown must be configured as an Interactive Filter. This allows the selected Breakdown value to dynamically filter all report widgets on the dashboard that are linked to the same indicator or dataset. Without enabling the interactive filter setting, the Breakdown will display data but will not filter the widgets.

Incorrect:
A. Breakdowns automatically filter report widgets so nothing needs to be done
Breakdowns do not automatically filter report widgets. By default, they display the segmented data but require explicit configuration as an interactive filter to affect widget data.

B. Edit the report widget to select the Breakdown to apply
Individual report widgets cannot be directly filtered by simply selecting a Breakdown in the widget settings. The filter effect comes from the dashboard-level interactive Breakdown configuration, not widget-level settings.

C. Create a separate dashboard for report widgets and apply the desired Breakdown in the dashboard properties
Creating a separate dashboard is unnecessary. Breakdowns can filter widgets on the same dashboard when configured as interactive filters. There is no need to duplicate dashboards to apply filtering.

Correct:
A. The Business Service Breakdown is associated to the “Number of open incidents“ Indicator, but is missing from the “Summed age of open incidents“ Indicator.
If a Breakdown is applied to only one of the two Indicators in a formula, the formula calculation for the Breakdown becomes inconsistent. Here, the “Number of open incidents“ has the Business Service Breakdown applied, but “Summed age of open incidents“ does not. This mismatch leads to higher or misleading Breakdown scores, because the formula divides a category-filtered numerator by an unfiltered denominator.

Incorrect:
B. There is nothing wrong. The Breakdown scores are correctly as is.
This is incorrect because the large difference in scores indicates a misconfiguration. The scores for Breakdown should generally align with overall patterns unless there is a missing or misapplied Breakdown.

C. The “Number of open incidents“ Indicator within the formula is set to apply Breakdowns when they are selected, while the “Summed age of open incidents“ Indicator is not set to apply Breakdowns.
This is similar to the correct option, but it describes the scenario in terms of “when selected,“ implying optional application. The real issue is the missing association of the Breakdown on one of the Indicators, not conditional application.

D. The “Summed age of open incidents“ Indicator within the formula is set to apply Breakdowns when they are selected, while the “Number of open incidents“ Indicator is not set to apply Breakdowns
This is the reverse scenario and would lead to understated or misaligned Breakdown scores, not the high values observed in the question. Therefore, it does not match the issue described.

Correct: C. After the Indicator Source conditions Additional conditions in Performance Analytics are evaluated only after the Indicator Source conditions have been applied. This means that during data collection, the system first filters records using the Indicator Source conditions. Once those records are identified, the Additional conditions are then applied to further refine the data set. This two-step filtering ensures that the Indicator targets the precise subset of data needed for accurate performance measurement.

Incorrect: A. At the same time as the Indicator Source conditions This is incorrect because the evaluation is sequential, not simultaneous. Indicator Source conditions are applied first to define the initial data set. Additional conditions are not evaluated concurrently—they act as a secondary filter after the source conditions.

B. When the Indicator is viewed in the Analytics Hub This option is misleading. Additional conditions are not evaluated at the time of viewing in the Analytics Hub. They are part of the data collection process, which occurs at scheduled intervals or when manually triggered. The Analytics Hub displays already collected and processed scores, not raw data subject to live filtering.

D. Before the Indicator Source conditions This is incorrect because it reverses the actual order of evaluation. The Indicator Source conditions always come first to define the scope of data. Applying Additional conditions before this would disrupt the intended logic and could lead to inaccurate results.

Correct: A. User Experience filter User Experience filters are designed to be applied across all visualizations within a workspace. These filters allow users to segment and refine data views based on user roles, departments, countries, or other attributes. Because they operate at the workspace level, they ensure consistency in filtering across multiple charts, reports, and dashboards, making them a powerful tool for comparative analysis and user-centric insights.

Incorrect: B. Signal Signals are used to highlight anomalies or trends in data, such as sudden spikes or drops. While they are useful for identifying noteworthy changes, they are not filters and cannot be applied across all visualizations. Signals are typically tied to specific indicators or widgets and serve more as alerts than universal filters.

C. Studio Studio is the development environment in ServiceNow used for creating and managing applications. It is not related to data visualization or filtering within Performance Analytics workspaces. Therefore, it cannot be used across visualizations in a workspace.

D. Dashboard Builder Dashboard Builder is a tool for creating and organizing dashboards. While it helps in structuring visualizations, it does not provide filtering capabilities that apply across all visualizations. Filters applied in Dashboard Builder are typically local to the dashboard or widget, not universal across a workspace.

Correct:
These three options accurately describe the creation and use of User Experience filters in ServiceNow Performance Analytics (PA), as per the CAS-PA Certification 2025 curriculum, which covers filter configuration, workspace integration, and access controls for personalized analytics experiences.

A. A single filter can be used across all the visualisations in a workspace.
User Experience filters in PA are designed for reusability within a specific workspace, allowing one filter to apply dynamically to multiple visualizations (e.g., dashboards or reports) sharing the same data model. This promotes consistency and efficiency in filtering across tabs or widgets in a workspace, without needing separate definitions.

B. Only users with the admin role can create User Experience filters.
Creation of User Experience filters requires elevated privileges, specifically the admin role, to ensure secure and controlled customization of workspaces in PA. This prevents unauthorized modifications and aligns with ServiceNow‘s governance standards for advanced configuration in the Now Experience UI Builder.

D. For filters to work in workspaces, you must configure an event handler to apply the filters.
To activate and enforce User Experience filters in workspaces, an event handler must be configured in the UI Builder. This handler ensures that filter interactions (e.g., user selections) trigger updates across associated visualizations, enabling interactive and responsive analytics as outlined in certification topics on event-driven PA interfaces.

Incorrect:
C. The filter you create in the Now Experience UI Builder is available in all workspaces.
This is incorrect because User Experience filters created in the Now Experience UI Builder are scoped to the specific workspace where they are built and cannot be automatically reused across different workspaces. Each workspace requires separate filter creation or configuration for portability, as emphasizing workspace isolation in PA configurations.

Correct: C. 1 > 7 > 2 > 3 > 8 > 6 > 5 > 4 This sequence reflects the proper setup flow for Text Analytics in Performance Analytics:

Setup Indicators and Fields – Begin by identifying which indicators and text fields will be analyzed.

Run Initial Index Collection – This step initializes the indexing of text data for analysis.

Configure Scheduled Collection – Set up automated jobs to collect and update text analytics data regularly.

Visualize in Word Cloud – Once data is indexed, initial visualizations like word clouds can be generated.

Review System Stop Words – Examine the default stop words that the system excludes from analysis.

Define Stop Words – Add any custom stop words specific to your organization or use case.

Define Phrases – Identify key phrases that should be tracked as single entities.

Save Phrases and Keywords Filter – Finalize and save the filters that will be used in ongoing analysis.

Incorrect: A. 1 > 2 > 3 > 8 > 6 > 5 > 7 > 4 This sequence incorrectly places the scheduled collection and visualization before the initial index collection. Without running the initial index collection, there’s no data to visualize or schedule for ongoing collection. Additionally, reviewing stop words before indexing is premature.

B. 1 > 7 > 3 > 8 > 6 > 5 > 4 > 2 Although this starts correctly with initial setup and indexing, it delays the scheduled collection until the very end. Scheduled collection should be configured early to ensure continuous data updates after the initial index.

D. 1 > 2 > 7 > 3 > 6 > 5 > 8 > 4 This option incorrectly places scheduled collection before the initial index collection. The initial index must be run before any scheduled jobs can effectively collect data. Also, reviewing system stop words should precede defining custom stop words and phrases.

Correct: D. Square brackets will follow all Breakdowns, curly brackets will not follow Breakdowns In Performance Analytics, square brackets [[ ]] are used to reference indicator scores that include breakdowns. This means the score retrieved will respect any breakdown filters applied, such as department, location, or assignment group. Curly braces {{ }}, on the other hand, are used to reference scores without applying breakdowns. This distinction is critical when calculating ratios or percentages where you want one part of the formula to be breakdown-specific and the other to be a general total.

Incorrect: A. Square brackets include all Time series, curly brackets only calculate the Daily Time series This is incorrect because both square and curly brackets can be used across various time series—daily, weekly, monthly, etc. The difference lies in whether breakdowns are applied, not the time series granularity.

B. Square brackets are used to choose the most recent score, curly brackets return the previous score in the time series This is misleading. Both bracket types can reference scores from any point in the time series depending on how the formula is constructed. They do not inherently select recent or previous scores based on their syntax.

C. There is no difference, either can be used This is incorrect. There is a functional difference between square and curly brackets in Performance Analytics formulas. Using them interchangeably would lead to inaccurate results, especially when breakdowns are involved.

Correct: D. Time frame Time frame refers to the overall period for which data is being analyzed—such as the last 30 days, last quarter, or fiscal year. While it influences what data is included in a report or visualization, it is not a defining element of a time series itself. A time series is structured around how data points are grouped and calculated over time, not the total span of time selected for analysis.

Incorrect: A. Range Range defines the number of data points or periods included in the time series. For example, a range of 12 with a monthly interval would display data for the past 12 months. This is a core component of time series configuration because it determines how far back the data should be collected and visualized.

B. Function Function refers to the mathematical operation applied to the data—such as SUM, COUNT, AVG, or MAX. This determines how the indicator values are calculated for each time interval. Without a function, the system wouldn’t know how to aggregate or interpret the data across time periods.

C. Interval Interval defines the granularity of the time series—such as daily, weekly, monthly, or quarterly. It sets the spacing between each data point in the series and is essential for structuring the timeline of the visualization. The interval directly affects how trends and patterns are displayed.

Correct:

A. Tables and columns
Why it‘s correct: Analytics Q&A is designed to interpret natural language queries and return relevant tables and columns from the Performance Analytics data model. This helps users identify the data source and structure behind their questions.

Example: A query like “How many incidents were resolved last week?” would surface the Incident table and relevant columns such as Resolved Date.

C. Indicators
Why it‘s correct: One of the core outcomes of Analytics Q&A is to suggest indicators that match the intent of the query. Indicators are the backbone of Performance Analytics, representing measurable data points like “Average resolution time” or “Number of open tasks.”

Example: A query like “Show me average resolution time for critical incidents” would return the corresponding indicator if configured.

D. Recent searches
Why it‘s correct: Analytics Q&A maintains a history of recent searches to improve usability and allow users to revisit previous queries. This feature enhances productivity and learning by showing what has been asked before.

Incorrect:

B. Breakdowns
Why it‘s incorrect: While breakdowns are essential in Performance Analytics for slicing data (e.g., by assignment group or priority), they are not a direct outcome of entering a query in Analytics Q&A. Breakdowns are applied after selecting an indicator or widget, not surfaced as a standalone result from a query.

Clarification: Analytics Q&A may suggest indicators that have breakdowns configured, but it does not return breakdowns themselves as primary query outcomes.

Correct:

D. Only fields specifically added in the Fields list
Why it‘s correct: In Performance Analytics, scripts only have access to fields explicitly added to the Fields list of the indicator source. This ensures controlled data access and optimized performance.

Implication: If a field is not listed, it won’t be available in the script context—even if it exists in the original record.

Best practice: Always curate the Fields list to include only necessary fields for scripting or breakdown logic.

Incorrect:

A. All fields from the original record
Why it‘s incorrect: PA scripts do not automatically inherit all fields from the fact table. This would introduce performance overhead and violate the principle of scoped access.

B. All fields and dot-walked fields from the original record
Why it‘s incorrect: Dot-walked fields (e.g., caller.email) require explicit inclusion in the Fields list. Including them without listing adds unnecessary joins and degrades performance.

C. Only reference and choice type fields from the original record
Why it‘s incorrect: Field type is irrelevant unless the field is explicitly added to the Fields list. Scripts can use any field type—reference, choice, string, date—if and only if it’s listed.

Correct:

B. Configure on-click behavior
Why it‘s correct: In Performance Analytics widgets, you can customize the on-click behavior to prevent redirection to the Analytics Hub. This is the recommended method to inhibit default navigation.

How it‘s done: Within the widget configuration, set the “On-click behavior” to a custom script or disable redirection entirely.

Use case: You may want to keep users on a dashboard or trigger a different action (e.g., open a report or modal) instead of navigating away.

Incorrect:

A. Configure a Report drill-down
Why it‘s incorrect: Report drill-downs apply to report widgets, not PA indicator widgets. They do not control Analytics Hub redirection behavior.

C. Disable Widget drilldown
Why it‘s incorrect: There is no native “disable drilldown” toggle for PA widgets. Drilldown behavior is managed via on-click scripting, not a blanket disable option.

D. Use ACLs to prevent the action
Why it‘s incorrect: Access Control Lists (ACLs) manage data access

Correct:

C. When there is no direct mapping between the Indicator field and the Breakdown table
Why it‘s correct: A scripted Breakdown Mapping is required when the indicator source field cannot be directly linked to the Breakdown table. This typically occurs when:

The field values don’t match the Breakdown table’s key field.

The relationship is complex or derived (e.g., calculated, conditional).

Use case: You want to break down incidents by a custom logic (e.g., “High Priority and Open” category) that doesn’t exist as a direct field.

Incorrect:

A. When the field to map to is of type Sys ID
Why it‘s incorrect: Sys ID is a common field type used in direct mappings. As long as the Sys ID in the indicator source matches the Breakdown table’s key field, no script is needed.

B. When the value needed for the Breakdown is available only as a dot-walked field
Why it‘s incorrect: Dot-walked fields (e.g., caller.department) can still be used in automated mappings if the field is accessible and matches the Breakdown table. Scripted mapping is only needed if the relationship is non-trivial or derived.

D. When the table being mapped is a database view and not an actual table
Why it‘s incorrect: Database views behave like tables in ServiceNow. As long as the view exposes the necessary fields and relationships, automated mapping is still possible. Scripted mapping is not inherently required just because it‘s a view.

Correct:

D. Only dashboards they have created or that have been shared with them
Why it‘s correct: Even users with the pa_admin role do not automatically gain access to all responsive dashboards. Access is governed by ownership and sharing settings.

Key behavior: Responsive dashboards must be explicitly shared with users or groups. The pa_admin role allows configuration and management, but not unrestricted viewing.

Best practice: Use dashboard sharing settings to control visibility across roles and users.

Incorrect:

A. Any dashboard created from a Homepage
Why it‘s incorrect: Homepages and responsive dashboards are distinct UI frameworks. Dashboards created from homepages do not automatically become responsive dashboards, nor are they universally visible to pa_admin users.

B. Any dashboard created by a user with pa_power_user role
Why it‘s incorrect: Dashboard visibility is not inherited from the creator’s role. Unless the dashboard is shared, pa_admin users cannot view it—even if created by a pa_power_user.

C. Any dashboard in the instance
Why it‘s incorrect: This would imply unrestricted access, which violates the sharing model of responsive dashboards. Even pa_admins must be granted access or be the creator.

pa_indicators write ACL below:

Correct:

C. Workspace
Why it‘s correct: Workspaces (e.g., Agent Workspace, CSM Workspace) are designed to support both Performance Analytics widgets and standard reports. They offer a unified, responsive interface where users can:

Embed PA visualizations (indicators, scorecards, breakdowns)

Include traditional reports (lists, charts, pivots)

Configure dashboards with interactive filtering and role-based visibility

Use case: A service desk agent can view real-time KPIs (PA) alongside tabular incident reports (Reporting) in a single Workspace.

Incorrect:

A. Visual Task Board
Why it‘s incorrect: Visual Task Boards are Kanban-style tools for task management. They do not support embedding PA or Reporting visualizations. Their focus is on workflow execution, not analytics.

B. CAB Workbench
Why it‘s incorrect: The Change Advisory Board (CAB) Workbench is a specialized interface for reviewing and approving changes. It may show some metrics, but it does not support configurable PA or Reporting visualizations.

D. Timeline Visualization
Why it‘s incorrect: Timeline Visualization is a report type, not a container or interface. It can be used in Reporting but cannot host PA widgets, nor does it support combined visualization.

Correct:

D. Breakdown Source
Why it‘s correct: Breakdown element security—which controls who can view specific breakdown elements (e.g., departments, assignment groups)—is configured in the Breakdown Source object.

Key configuration: Within the Breakdown Source, you can define:

Element-level security rules

Role-based visibility

Conditions for filtering breakdown elements

Use case: You want only users with the itil role to see breakdown elements related to “Network Support.” This is set in the Breakdown Source properties.

Incorrect:

A. Automated Indicator
Why it‘s incorrect: Indicators define what is measured, not how breakdown elements are secured. They reference breakdowns but do not control element-level access.

B. Manual Breakdown
Why it‘s incorrect: Manual Breakdowns are populated manually, but security is still governed by the Breakdown Source. The Breakdown object itself doesn’t hold security logic.

C. Automated Breakdown
Why it‘s incorrect: Automated Breakdowns are populated dynamically from source fields, but again, security is managed via the Breakdown Source, not the Breakdown object.

Correct:

A. Line Chart
Why it‘s correct: Line Charts are ideal for visualizing time series data, showing trends over time with continuous data points.

Use case: Tracking incident volume or SLA compliance across weeks or months.

B. Column Chart
Why it‘s correct: Column Charts are also supported for time series widgets, especially when you want to compare discrete time intervals (e.g., daily or monthly counts).

Use case: Comparing number of requests per day over the past 30 days.

Incorrect:

C. Scorecard
Why it‘s incorrect: Scorecards display single aggregated values, not trends over time. They are used for KPIs like “Open Incidents” or “Average Resolution Time,” not time series visualization.

D. Treemap
Why it‘s incorrect: Treemaps are used for hierarchical or proportional data, not time-based trends. They are not supported in Time Series Analytics Widgets.

Correct:

D. On both the KPI Details and time series widgets
Why it‘s correct: Global targets are configured to provide benchmark or goal values across indicators. These targets appear:

In the KPI Details view, helping users compare actual values against targets.

In time series widgets, where targets are visualized as horizontal lines or markers across the timeline.

Use case: A target of “< 5 open critical incidents” will show in both the detailed KPI view and the trend chart. Incorrect: A. In Interactive Filters
Why it‘s incorrect: Interactive Filters are used to filter data across dashboards (e.g., by assignment group or priority). They do not display global targets.

B. Only on the KPI Details
Why it‘s incorrect: While KPI Details do show global targets, limiting visibility to only this view is incorrect. Targets are also rendered in time series widgets.

C. Only in time series widgets
Why it‘s incorrect:

Correct:

A. Show threshold
Why it‘s correct: This setting enables the display of threshold lines on the chart, helping users visually compare actual values against defined targets or limits.

Use case: Displaying a red line at “20 open incidents” to indicate SLA breach threshold.

C. Show trend
Why it‘s correct: This option overlays a trend line (e.g., linear regression) to help visualize the overall direction of the data over time.

Use case: Identifying whether incident volume is increasing or decreasing over the past 30 days.

D. Show data labels
Why it‘s correct: This setting displays numeric values directly on the chart points or bars, improving readability and precision.

Use case: Showing exact incident counts on each column in a time series widget.

Incorrect:

B. Show visualization selector
Why it‘s incorrect: Time Series Analytics Widgets are predefined with specific chart types (e.g., line or column). They do not support dynamic visualization switching via a selector.

E. Show breakdown selector
Why it‘s incorrect: Breakdown selectors are used in Breakdown Widgets, not Time Series Widgets. Time Series Widgets display data over time, not across breakdown elements.