Correct:

A. Service Management This is a widely available and foundational Performance Analytics Content Pack. It includes dashboards and indicators for core ITSM processes such as Incident, Problem, Change, and Request Management, helping organizations monitor service delivery and operational efficiency.

B. HR Management Correct. The HR Performance Analytics Content Pack provides prebuilt KPIs and dashboards for tracking HR case volumes, resolution times, employee satisfaction, and other HR service delivery metrics. It supports HR Service Delivery applications and is commonly used in onboarding and employee support scenarios.

E. Security Operation Management Correct. This content pack supports Security Operations, including Incident Response, Threat Intelligence, and Vulnerability Response. It provides actionable dashboards and indicators to monitor security posture and response effectiveness.

Incorrect:

C. Software Asset Management Incorrect.
Software Asset Management (SAM) does not have a dedicated Performance Analytics Content Pack. While SAM data can be visualized using custom indicators and dashboards, it is not supported by an out-of-the-box content pack.

D. Governance, Risk and Compliance Incorrect. Although GRC applications are supported in ServiceNow, there is no standard Performance Analytics Content Pack specifically labeled for GRC. GRC dashboards are typically built using custom configurations or via Advanced Risk Management solutions, not through a predefined content pack.

Correct:

A. Breakdown Mappings are required to apply Automated Breakdowns to Automated Indicators
Correct. Breakdown Mappings are essential when applying Automated Breakdowns to Automated Indicators. They define how the breakdown elements (e.g., assignment groups, categories) are linked to the indicator’s data source (facts table), enabling the indicator to segment scores accordingly.

B. Breakdown Mapping may use a script
Correct. Breakdown Mappings can include scripts to dynamically determine how breakdown elements relate to the indicator data. This is especially useful when the relationship is not direct or requires transformation logic (e.g., mapping a user to their department via scripting).

D. Multiple Breakdown Mapping allow the same Breakdown to be used against multiple Indicator Facts tables
Correct. You can create multiple Breakdown Mappings for the same Breakdown, each targeting a different facts table. This allows reuse of breakdown definitions across various indicators with different data sources, promoting consistency and reducing duplication.

Incorrect:

C. Breakdown Mappings are used to define Breakdown Relations
Incorrect. Breakdown Relations are configured separately and define hierarchical relationships between breakdowns (e.g., Manager ? Group). They are not defined through Breakdown Mappings, which instead link breakdowns to indicator data sources.

E. Breakdown Mapping allow Automated Breakdowns to be used with Manual Indicators
Incorrect.Manual Indicators do not support Automated Breakdowns. Manual Indicators rely on manually entered scores and breakdowns must be manually assigned. Breakdown Mappings apply only to Automated Indicators with system-collected data.

Correct:

B. The Formula Indicator score is calculated when the Indicator is viewed This is the true statement about Formula Indicators in Performance Analytics. Unlike Automated Indicators, which collect and store scores during scheduled collection jobs, Formula Indicators calculate their scores dynamically at runtime—when the indicator is viewed on a dashboard or report. This allows them to reflect the most current values of the referenced indicators and ensures flexibility in combining multiple data points without storing redundant scores.

Incorrect:

A. A Formula Indicator can reference the Indicator Threshold value Incorrect. Formula Indicators do not reference threshold values directly. Thresholds are used for score evaluation and visualization (e.g., coloring or alerts), but they are not part of the formula logic itself. Formula Indicators reference other indicators, not their thresholds.

C. A Formula Indicator can set up to 5 Automated Indicators Incorrect. There is no fixed limit of 5 Automated Indicators in a Formula Indicator. You can reference multiple indicators as needed, depending on the complexity of the formula. The number is not capped at 5.

D. A Formula Indicator is needed anytime you need to calculate an aggregate Incorrect. Aggregates like SUM, COUNT, AVG are typically handled by Automated Indicators using aggregates in the Indicator Source. Formula Indicators are used to combine or transform existing indicator scores, not to perform basic aggregation from raw data.

Correct:

C. Targets configuration page This is the correct location to set a target for a specific breakdown element—such as Incident Assignment Group—on the ‘Number of open incidents’ indicator. The Targets configuration page within KPI Details allows you to define:

Global targets (applied across all breakdowns)

Breakdown-specific targets (e.g., different targets per Assignment Group)

Personal targets (user-specific)

This is essential for tailoring performance expectations per group or role, and it aligns with CAS–PA best practices for target-driven KPI monitoring.

Incorrect:

A. Indicator Groups list Incorrect. The Indicator Groups list is used for organizing indicators into logical groupings for navigation or reporting purposes. It does not provide any functionality for setting targets.

B. KPI Composer module Incorrect. The KPI Composer is used for visualizing and designing KPI relationships, such as linking indicators to business goals or initiatives. It does not handle target configuration.

D. KPI Signals Configuration page Incorrect. This page is used to configure statistical signal detection (e.g., short run, long run, outliers) for indicators. It helps monitor anomalies, but it does not define performance targets.

Correct:

C. Contributing Indicators This is the correct related list within a Formula Indicator record used to navigate to the indicators referenced in the formula and their associated indicator sources. The Contributing Indicators list provides a direct view of all indicators that are part of the formula expression, allowing administrators to quickly access and manage the underlying data sources and configurations. This is essential for maintaining traceability and ensuring the formula logic aligns with the intended data inputs—an important concept for CAS–PA certification.

Incorrect:

A. Indicator Groups Incorrect. Indicator Groups are used to organize indicators into logical collections for reporting or navigation purposes. They do not provide visibility into formula components or their sources.

B. Managed Sources Incorrect. This list is associated with Indicator Sources, not Formula Indicators. It shows which indicators are using a particular source, but it does not help navigate from a Formula Indicator to its contributing indicators.

D. Breakdowns Incorrect. The Breakdowns related list shows which breakdowns are applied to the Formula Indicator, but it does not reveal the indicators or sources used in the formula logic. It’s focused on data segmentation, not formula composition.

Correct:

D. No role This is the correct answer. In ServiceNow Performance Analytics, any user who has access to an indicator on the Analytics Hub can set their own personal targets and thresholds—no additional role is required. This feature is designed to empower individual users to define their own performance expectations and benchmarks, enhancing personalization and self-driven analysis. It aligns with CAS–PA principles of user autonomy and flexible KPI tracking.

Incorrect:

A. pa_threshold_admin Incorrect. This role is used to manage global thresholds across indicators, not personal ones. It allows configuration of threshold rules and logic but is not required for users to set their own thresholds in Analytics Hub.

B. pa_target_admin Incorrect. This role is intended for administrators managing global or breakdown-specific targets, not personal targets. It enables centralized control over organizational KPIs but does not govern personal target creation.

C. pa_viewer Incorrect. While this role allows users to view indicators and dashboards, it is not a prerequisite for setting personal targets. Any user with indicator access—regardless of role—can define personal targets and thresholds.

Correct:

B. Choice [sys_choice]
This is the most suitable Facts table to use as the Breakdown Source when creating a Breakdown for Close codes in Incident records. In ServiceNow, Close codes are typically implemented as choice values for a specific field (e.g., close_code on the Incident table). The sys_choice table stores all selectable options for fields across tables, making it the ideal source for creating a Breakdown that reflects all possible Close code values. This ensures the Breakdown dynamically reflects the configured choices without hardcoding or manual entry.

Incorrect:
A. Task [task]
Incorrect. While Incident records extend from the Task table, the Task table itself does not contain the Close code definitions. It holds task-level data but is not suitable for sourcing Breakdown elements based on field choices.

C. Root Cause [sys_root_cause]
Incorrect. This table is used for Problem Management and stores root cause records. It is unrelated to Incident Close codes and would not provide the correct Breakdown elements.

D. Incident [incident]
Incorrect. Although Close codes are used in Incident records, the Incident table contains actual data records, not the list of possible Close code values. Using it as a Breakdown Source would only reflect Close codes that have already been used, not the full set of available options.

Correct:

A. The KPI score on the Target Review date is compared to the Target This is the correct behavior in ServiceNow Performance Analytics when evaluating whether a target has been met for a KPI with a Review date in the past. The system compares the actual KPI score recorded on the Review date against the defined target value. If the score meets or exceeds the target (depending on the KPI logic), it is considered met. This approach ensures consistency and aligns with CAS–PA best practices for time-bound performance evaluation.

Incorrect:
B. The target owner sets a met/not met flag Incorrect. While users can view target status, the system automatically determines whether the target is met based on score comparison. Manual flagging is not part of the KPI Details logic for target evaluation.

C. The latest KPI score is compared to the average score for the period from Target Start date to Review date Incorrect. This introduces two unrelated metrics—latest score and average score—neither of which is used for evaluating past targets. The system uses the score on the Review date, not an average or the latest score.

D. The average score for the selected time period is compared to the Target Incorrect. While averages may be used in other contexts, target evaluation for a past Review date is based on the specific score on that date, not an average over time.

Correct:

A. Currently Active Incidents with Open Problems Correct. This type of snapshot measurement—filtering for currently active incidents that are linked to open problems—can be easily achieved using Reporting in ServiceNow. Reports can query live data using conditions across related tables, making this a valid use case for standard reporting.

E. Current view of Employees by Department Correct. Reporting can generate real-time tabular or graphical views of employees segmented by department using reference fields. This is a classic example of a list or pivot-style report that reflects current organizational structure.

Incorrect:

B. Weekly comparison of Incident resolution time vs SLA met percentage Incorrect. This requires time-series analysis and correlation between multiple indicators, which is beyond the scope of standard Reporting. It’s best handled using Performance Analytics, which supports historical trending and KPI comparisons over time.

C. Department Headcount Over Time Incorrect. Reporting shows current data, not historical trends. To measure headcount changes over time, you need Performance Analytics with scheduled data collection and time-based indicators.

D. Forecast of HR Case Resolution Times over the Next month Incorrect. Forecasting is a predictive analytics capability, not supported by standard Reporting. This requires Performance Analytics with forecasting features, which can project future values based on historical trends.

Correct:

C. Unlimited, with a warning if you have selected more than 10 This is the correct behavior when Collect breakdown matrix is enabled for an Indicator in Performance Analytics. You can select an unlimited number of breakdowns, but ServiceNow will display a warning if you choose more than 10, due to potential performance impact during data collection. The breakdown matrix feature collects scores for every combination of selected breakdown elements, which can grow exponentially with more breakdowns, so the system cautions administrators to avoid excessive combinations.

Incorrect:

A. You cannot relate any breakdowns if Collect breakdown matrix is enabled Incorrect. Enabling Collect breakdown matrix is specifically intended to relate multiple breakdowns and collect scores for their combinations. This option requires breakdowns, not prohibits them.

B. Unlimited Partially true but incomplete. While technically unlimited breakdowns can be selected, this option ignores the important system warning that appears after selecting more than 10. The correct answer must acknowledge that limit advisory.

D. There is a hard limit of 10 Incorrect. There is no hard-coded limit of 10 breakdowns. The system allows more but warns the user due to performance concerns. This answer falsely implies a strict cap.

Correct:

B. Linear Correct. The Linear forecasting method uses a straight-line projection based on historical data trends. It’s suitable for KPIs with consistent growth or decline and is one of the standard forecasting models available in Performance Analytics.

C. Seasonal Trend Correct. The Seasonal Trend method accounts for repeating patterns in the data over time (e.g., monthly spikes, quarterly dips). It combines seasonality with trend analysis, making it ideal for KPIs affected by cyclical behavior.

D. Random Forest Correct. Random Forest is a machine learning-based forecasting method available in Performance Analytics. It builds multiple decision trees and aggregates their predictions, offering robust forecasting for complex, non-linear patterns.

E. Seasonal Correct. The Seasonal method focuses purely on recurring patterns without factoring in long-term trends. It’s useful when the KPI exhibits regular fluctuations but no consistent upward or downward movement.

Incorrect:

A. Random Incorrect. There is no forecasting method called “Random” in ServiceNow Performance Analytics. Forecasting relies on structured models, not random or stochastic projections. This option is invalid and does not reflect any supported method.

Correct:
A. Order Correct. The Order field is used to define the sequence in which Report Range values appear. This is important for ensuring that ranges are displayed logically (e.g., Low ? Medium ? High) in reports and dashboards.

C. Label Correct. The Label field defines the display name for each range value. It’s what users see in the report output (e.g., “Critical”, “Moderate”, “Low”), making it essential for clarity and usability.

D. Value List Correct. The Value List field specifies the actual values that fall within the defined range. This is how the system knows which data points belong to each labeled range, enabling accurate filtering and grouping.

Incorrect:

B. Upper value date Incorrect. This is not a valid field when defining a Report Range. Report Ranges are typically based on discrete values or value lists, not date boundaries. Date-based filtering is handled differently in reports.

E. Lower value int Incorrect. There is no field named “Lower value int” in the Report Range configuration. Numeric boundaries are not defined this way; instead, ranges are built using Value Lists or scripted logic if needed.

Correct:

D. Send a notification email and type message text Correct. When granting access to a Performance Analytics dashboard, you can send a notification email to the recipient and customize the message text. This helps inform users of their new access and provide context or instructions. It’s a supported feature in the dashboard sharing interface.

E. Specify one of the following: Can edit / Can view Correct. You can assign specific access levels when sharing a dashboard:

Can view: User can view the dashboard but not modify it.

Can edit: User can modify the dashboard layout and widgets. These access levels control user permissions and are part of the standard dashboard sharing configuration.

Incorrect:

A. Send a notification email using a predefined template Incorrect. While you can send a notification email, it is not based on a predefined template. The message is customizable, and there is no fixed template system for dashboard sharing notifications.

B. Specify one of the following: Can edit / Can view / Can share Incorrect. The “Can share” option is not available when granting dashboard access. Users can be given view or edit rights, but not the ability to re-share the dashboard unless they are owners or admins.

C. Specify Share until future date Incorrect. There is no time-bound sharing option for dashboards. You cannot specify an expiration date for access. Dashboard sharing is manual and persistent until revoked.

Correct:

B. It must match exactly the Valid for Frequency setting of the Indicator Source This is the correct statement for configuring the Frequency setting of an Indicator in ServiceNow Performance Analytics. The Indicator Frequency (e.g., daily, weekly, monthly) must exactly match one of the Valid for Frequency values defined in the Indicator Source. This ensures that the Indicator can properly collect scores from the source at the intended intervals. If the frequencies do not match, the Indicator will not be able to collect data, leading to gaps or errors in score generation.

Incorrect:

A. It must be the same or larger than the Valid for Frequency setting of the Indicator Source Incorrect. There is no hierarchy or scaling logic between frequencies. The Indicator Frequency must match exactly, not be larger or smaller. For example, a weekly indicator cannot collect from a source valid only for daily frequency.

C. It must be the same or smaller than the Valid for Frequency setting of the Indicator Source Incorrect. Again, this implies a scaling relationship that does not exist. The system requires an exact match, not a relative comparison.

D. It must match one of the selected Valid for Frequency options of the Indicator Source Incorrect. While the Indicator Source can support multiple frequencies, the Indicator itself must be configured to match exactly one of those. This answer is misleading because it suggests partial compatibility without enforcing the exact match requirement.

Correct:

A. Act as interactive filter Correct. This customization allows a Report Widget to function as an Interactive Filter, meaning it can drive filtering behavior across other widgets on the dashboard. When enabled, clicking elements in the report (e.g., bars, pie slices) will apply filters to connected widgets.

B. Title Alignment Correct. You can customize the alignment of the widget title (e.g., left, center, right) to match dashboard layout preferences. This is a valid visual customization option for Report Widgets.

D. Show Border / Header / Title Correct. This option lets you toggle the visibility of the widget’s border, header, and title, helping streamline the dashboard’s appearance or emphasize specific widgets.

Incorrect:

C. Set Widget Border Thickness Incorrect. While you can show or hide the border, customizing the thickness of the widget border is not a supported option in Report Widget settings.

E. Follow element Incorrect. “Follow element” is not a valid Report Widget customization. It’s not part of the widget configuration options and may be confused with unrelated platform features.

Correct:

D. Indicator Source and Indicator This is the correct configuration scope for defining Text Analytics Stop Words in Performance Analytics. Stop Words—commonly used words like “the,” “and,” or domain-specific terms—can be configured at two levels:

Indicator Source level: Filters out irrelevant terms during text analysis across all indicators using that source.

Indicator level: Allows fine-tuned control for specific indicators that may require unique stop word exclusions.

This dual-level configuration ensures flexibility and precision in text analytics, aligning with CAS–PA best practices for semantic filtering and relevance scoring.

Incorrect:

A. Indicator Source only Incorrect. While you can configure Stop Words at the Indicator Source level, this option alone is too limited. It doesn’t allow customization for individual indicators that may need different exclusions.

B. Indicator Source and globally Incorrect. There is no global stop word configuration in Performance Analytics. Stop Words must be defined per Indicator Source or per Indicator, not system-wide.

C. All stop words are defined globally Incorrect. This is factually wrong. ServiceNow does not support global Stop Word definitions for Text Analytics. All configurations are scoped to specific sources or indicators.

Correct:

A. pa_dashboards This is the correct table that stores Dashboard records in ServiceNow Performance Analytics. The pa_dashboards table holds metadata and configuration details for each dashboard, including layout, shared access, and widget references. It is the authoritative source for dashboard definitions and is directly referenced when managing or querying dashboards in the platform.

Incorrect:

B. pa_dashboard Incorrect. This table does not exist in the ServiceNow schema. It may appear plausible due to naming conventions, but the correct table name is plural: pa_dashboards.

C. pa_scorecard Incorrect. The pa_scorecard table is used for Scorecards, which are a different visualization component in Performance Analytics. Scorecards display indicator scores and trends but are not dashboards.

D. pa_scorecards Incorrect. Similar to option C, this refers to Scorecard records, not dashboards. It is unrelated to dashboard layout or configuration.

Correct:

B. Ranking tasks based on multiple attributes This is the correct use case for Spotlight in ServiceNow Performance Analytics. Spotlight is designed to rank and prioritize records (typically tasks) based on a weighted combination of multiple attributes—such as urgency, impact, assignment group, or age. It helps users focus on the most critical or relevant items by dynamically scoring and sorting them.

Incorrect:
A. Measuring SLA attainment Incorrect. SLA attainment is best tracked using Performance Analytics indicators and SLA reports, not Spotlight. SLA metrics are typically time-based and binary (met/not met), which do not require multi-attribute ranking.

C. Identifying high risk changes Incorrect. While high-risk changes can be flagged using risk assessment logic or change risk indicators, Spotlight is not the tool for risk identification. Instead, use risk calculation rules or change risk dashboards.

D. Deflecting incidents Incorrect. Incident deflection is handled through Virtual Agent, Knowledge Base, or Predictive Intelligence, not Spotlight. Spotlight does not support deflection workflows or automation—it’s focused on ranking existing tasks, not preventing them.

Correct:

C. Set a review date on which to consider updating the target Correct. In Analytics Hub, when configuring a target for an indicator, you can specify a Review Date. This helps define when the target should be evaluated or reconsidered, aligning with performance cycles or strategic reviews.

D. Set a start date in the future Correct. You can configure a future Start Date for a target, allowing it to become active at a later time. This is useful for planning phased performance goals or aligning targets with upcoming initiatives.

E. Set the improvement as a percentage Correct. Targets can be defined as a percentage improvement over a baseline (e.g., current score or average score). This allows for relative performance goals, such as “increase resolution rate by 10%,” which is a common use case in Performance Analytics.

Incorrect:

A. Select 3 answers from the below options Incorrect. This is not an action you perform in Analytics Hub—it’s part of the question format, not a valid configuration option.

B. Set the threshold as an improvement on the average score Incorrect. Thresholds and targets are distinct in Performance Analytics:

Thresholds define visual or alert boundaries (e.g., red/yellow/green zones).

Targets define performance goals. You cannot set a threshold as an improvement over an average score; that logic applies to targets, not thresholds.

Correct:

A. All associated Indicators must be removed from the source This is the correct requirement when changing the Valid for frequency property on an Indicator Source in ServiceNow Performance Analytics. The system enforces this restriction because each Indicator tied to the source is configured to collect scores at a specific frequency (e.g., daily, weekly). Changing the frequency on the source without first removing the Indicators would create a mismatch and potentially corrupt score collection logic. Therefore, you must remove all associated Indicators before modifying the frequency

Incorrect:

B. Nothing, you can change the Valid for frequency at any time Incorrect. This disregards the system-enforced dependency between Indicator Source frequency and associated Indicators. You cannot change the frequency freely without first removing the Indicators.

C. All associated Indicators must be set to the new frequency Incorrect. You cannot change the frequency of the Indicators directly to match a new source frequency. The correct process is to remove the Indicators, update the source frequency, and then reconfigure or re-add Indicators as needed.

D. Scores must be deleted for all Indicators using the Indicator Source Incorrect. Score deletion is not required to change the Valid for frequency setting. The system only requires that Indicators be removed from the source, not that historical data be purged.

Correct:

B. Targets Configuration This is the correct panel within KPI Details where you can view, edit, and align multiple targets for the same indicator. If you have different targets set for various breakdowns (e.g., Assignment Groups, Departments), the Targets Configuration panel allows you to modify them to have the same value, ensuring consistency across performance expectations. This is a key capability for managing KPI goals in Performance Analytics.

Incorrect:

A. Filters Configuration Incorrect. This panel is used to define data filters applied to the indicator (e.g., only incidents with priority 1). It does not manage targets or their values.

C. Thresholds Configuration Incorrect. Thresholds are used to define visual boundaries (e.g., red/yellow/green zones) for score interpretation. They are not the same as targets, and this panel does not allow you to modify target values.

D. KPI Signals Configuration Incorrect. This panel is used to configure statistical signal detection (e.g., outliers, trends, runs) for the indicator. It helps identify anomalies but does not manage or modify targets.

Correct:

D. Pie Correct. Pie is a valid widget visualization type in ServiceNow Performance Analytics. It is commonly used to display categorical distributions, such as incident counts by priority or cases by department. Pie charts are supported in Report Widgets and Performance Analytics Widgets, making them a standard visualization choice for dashboards.

Incorrect:

A. Breakdown Incorrect. Breakdown is not a visualization type—it refers to a data segmentation mechanism used in Performance Analytics to filter or group scores (e.g., by Assignment Group, Category). It can be applied to widgets but is not itself a visualization.

B. Time series Incorrect. While time-based visualizations (like line or area charts) are valid, “Time series” is not a named visualization option in the widget configuration. Instead, you would select Line, Spline, or Column to represent time series data.

C. Score Incorrect. Score is a data value, not a visualization type. You can display scores using visualizations like Scorecards, Single Score Widgets, or Time Series Charts, but “Score” is not a standalone visualization choice.

Correct (NOT accurate):

D. You can only set the maximum number of groups for multiple data sources individually per source in bar visualisations This statement is NOT accurate. In ServiceNow Performance Analytics, when configuring bar visualizations with multiple data sources, the maximum number of groups is set globally for the widget, not individually per source. You cannot configure a separate group limit for each data source within the same bar chart. This limitation ensures consistent rendering and performance across the visualization.

Accurate Statements

A. You can visualise historical and real-time process statistics in role-based dashboards Correct. Performance Analytics supports both historical trending and real-time score updates, and dashboards can be role-based, showing relevant KPIs and visualizations tailored to user roles (e.g., Service Desk Manager, HR Agent).

B. You can only add one data source for the pie and donut visualisations Correct. Pie and donut charts are limited to a single data source. They are designed to show proportional distributions from one indicator or report, not comparative data across multiple sources.

C. You can configure a workspace to use Performance Analytics visualisations Correct. You can embed Performance Analytics widgets into Workspaces (e.g., Agent Workspace, HR Workspace) to provide contextual insights and KPI tracking directly within the workspace interface.

Correct:

B. August 1st, 2020 This is the correct value of score_start for a Monthly Indicator using the specified Collection parameters. When the Indicator job runs on September 1st, 2020, and both Relative Start and Relative End are set to “1 days ago”, the system evaluates the score for the previous day, which is August 31st, 2020. However, because the Indicator is Monthly, the score collected for August 31st is attributed to the entire month of August, and thus the score_start is set to August 1st, 2020—the beginning of that monthly period.

This behavior aligns with how Performance Analytics aggregates scores for time-based indicators: the score_start reflects the start of the period being scored, not the date the data was collected.

Incorrect:

A. August 31th, 2020 Incorrect. This is the date of data collection, not the score_start. For Monthly Indicators, the score_start must reflect the start of the month, not the specific day of collection.

C. September 30th, 2020 Incorrect. This is the end of the current month, which is not relevant to the score being collected for August. The job is collecting data for the previous day, which belongs to August.

D. September 1st, 2020 Incorrect. This is the job execution date, not the score_start. The score_start must reflect the start of the month being scored, which is August.

A. Can be multiple per indicator
Thresholds allow multiple configurations per Indicator (e.g., separate ranges for warning, critical, and optimal levels across different metrics). This enables granular alerting without limiting to a single range, facilitating complex monitoring scenarios such as tiered escalation rules in performance dashboards.

C. Summary notification about hit with a list of indicators
Thresholds can generate summary notifications (e.g., via email or in-platform alerts) that list multiple Indicators that have triggered a threshold hit, providing aggregated insights for incident management or trend analysis. This feature supports bulk alert handling in PA, especially for dashboards with interdependent KPIs.

D. It doesn‘t usually change
Threshholds are typically static definitions (e.g., fixed numerical bounds like 80-100%) set manually and not auto-evolving, ensuring consistent baseline comparisons over time. They differ from dynamic elements like scores, reducing volatility in alerting logic as emphasized in certification guidelines on threshold stability for reliable automation.

Incorrect Options
B. Can evolve change over time
Statistics Thresholds do not typically change over time; they are predefined and static to maintain stable alert criteria. While scores evolve with data collection, thresholds remain fixed unless manually updated, contrary to adaptive features in other PA elements like forecasts or dynamic baselines.

E. Calculates gap
Thresholds define if a score falls within, above, or below a range and trigger alerts, but they do not calculate numerical gaps (e.g., the difference between actual and target values). Gap calculations are associated with Targets or other comparative features in PA, not thresholds themselves, as per the certification‘s distinctions between alert triggers and value comparisons.

Correct:
B. When there is no field in the Indicator Facts table that matches the value to be bucketed
A script is required when the data needed for the bucket logic does not exist as a direct field on the Indicator Facts table. Bucket Group Breakdowns categorize values into logical buckets (for example: 0–3 days, 4–7 days, 8+ days). If the system cannot map those buckets using an existing field in the facts table, a script must be used to dynamically determine and assign the correct bucket for each record.

Incorrect:
A. When needing to limit the visibility of the Buckets
Limiting visibility is related to roles, sharing, or dashboard permissions, not Breakdown Mapping. Visibility requirements do not trigger the need for a script.

C. When mapping to a Manual Indicator
Manual Indicators store manually entered scores and do not require scripted mapping for Bucket Breakdowns, because Bucket logic applies to records in fact tables, not manually entered values.

D. When the Breakdown is a Manual Breakdown
Manual Breakdowns are populated manually with their own specific values, and do not require scripting for mapping. Scripting applies only when automatic mapping to records is not possible through existing fields in the facts table.

Correct:
C. PA Power User
PA Power Users have the ability to create and maintain Performance Analytics content, including updating and manually entering scores for Manual Indicators. Their role grants elevated write permissions, allowing them to directly manage indicator data where manual scoring is required.

D. PA Contributor
PA Contributors are allowed to manually submit or update data that feeds into indicators, including Manual Indicators. This role is specifically intended for users who update operational or performance data without needing broader administrative capabilities in Performance Analytics.

Incorrect:
A. PA Viewer
PA Viewers have read-only access and can only view dashboards, Analytics Hub content, and indicator scores. They cannot modify or manually enter scores for any indicators, including Manual Indicators.

B. PA Threshold Admin
PA Threshold Admins are responsible for configuring thresholds, such as warning or critical limits on indicators. Their permissions do not include entering or modifying Manual Indicator scores.

E. PA Data Collector
PA Data Collectors are used for automated data collection jobs and do not have permissions for manual score entry. They focus on system-driven indicator data collection rather than user-driven manual scoring.

Correct:
B. Set Title Color
Users can customize the appearance of a PA widget by modifying the title color. This is a supported configuration option that helps visually distinguish widgets on a dashboard.

D. Set Header Color
PA widgets support header styling options, including the ability to set the header color. This allows for consistent visual themes or emphasis on priority widgets.

E. Show Border / Header / Title
Users can enable or disable the display of widget elements such as the border, header, or title. These display controls are part of standard widget configuration options and allow for greater flexibility in dashboard layout and visual clarity.

Incorrect:
A. Set Widget Border Thickness
While you can show or hide a widget border, customizing border thickness is not a supported PA widget configuration option. The platform does not provide this level of border styling.

C. Show Data Table
This is not a universal widget option. Only certain widget types (like list or score widgets) inherently display tabular data, but there is no general “Show Data Table” toggle for PA widgets. The platform provides controls for visual elements, not raw table toggles for all widget types.

Correct:
C. It is active by default
KPI Signals is already activated by default in Performance Analytics. Administrators do not need to install a plugin, request it, or perform any extra activation step. KPI Signals is available out-of-the-box and ready to use to help identify unusual patterns and anomalies in indicator scores.

Incorrect:
A. Activate the sn-kpi-signals plugin
There is no separate plugin that must be manually activated for KPI Signals. Since the feature is enabled by default, this step is not required.

B. Request from the ServiceNow Store
KPI Signals is not a Store application. It is a native Performance Analytics capability, so it does not need to be downloaded or requested through the Store.

D. Raise a ServiceNow Support (HI) request
There is no need to contact ServiceNow Support or raise a HI request. KPI Signals does not require manual activation or backend intervention.

Correct:
A. PA Admin
PA Admin has full administrative capabilities within Performance Analytics, including the ability to create and manage Global Thresholds. This role can configure thresholds that apply across indicators, giving it control over platform-wide performance conditions such as warning, critical, or target levels.

E. PA Power User
PA Power Users can configure analytics content, including the ability to create Global Thresholds. Their role supports building and maintaining Performance Analytics configurations, which includes defining thresholds to highlight performance deviations in dashboards and Analytics Hub.

Incorrect:
B. PA Viewer
PA Viewers have read-only access. They can view dashboards, indicators, and thresholds, but they cannot create or modify any threshold configurations, including Global Thresholds.

C. PA Data Collector
PA Data Collector is used to run automated data collection jobs. This role has no permissions for creating or managing Global Thresholds and is limited to supporting data collection activities.

D. PA Target Admin
PA Target Admin is responsible for creating and managing Targets, not Thresholds. While both are used to track performance expectations, Targets and Thresholds are separate features in PA. Therefore, this role cannot create Global Thresholds.

Correct:

C. One global and one personal Target per user, per date This is the accurate configuration limit for daily targets on an indicator without any breakdown. In Performance Analytics, when no breakdown is applied, the system supports one global target (shared across all users) and one personal target (specific to each user) per day. This ensures clarity and avoids conflicts in target evaluation, allowing both organizational benchmarks and individual goals to coexist without overlap.

Incorrect:

A. Multiple personal and multiple global Targets per user, per date This is incorrect. Performance Analytics does not support multiple targets of the same type (global or personal) for the same user and date when no breakdown is configured. Allowing multiple targets would create ambiguity in score evaluation and violate the one-target-per-type-per-date rule.

B. A single Target only This is too restrictive. While only one target per type is allowed, the system does support both one global and one personal target per user per date. Saying “a single target only“ ignores the valid dual-target configuration.

D. One global Target and multiple personal Targets per user, per date This is incorrect. While one global target is allowed, only one personal target per user per date is permitted. Multiple personal targets would conflict with the system’s target resolution logic and are not supported in standard PA configuration.

Correct:

B. Choice list This is the appropriate filter type for Priority, which is typically a predefined field with discrete values (e.g., Critical, High, Medium, Low). The Choice list interactive filter allows users to select from these predefined options, making it ideal for filtering incident records based on priority.

C. Date This is the correct filter type for Age, which is derived from date fields such as “Opened“ or “Resolved.“ The Date interactive filter enables users to specify ranges (e.g., last 7 days, this month) or exact dates to filter records based on their age. It supports dynamic and static date filtering, which is essential for time-based analysis.

Incorrect:

A. Reference Reference filters are used for fields that point to another table (e.g., Assigned to ? User table). Priority is a choice field, not a reference field, and age is derived from date fields, so Reference is not suitable for either.

D. List List filters are used to filter records based on a list of values, but they are not optimized for fields like Priority (which uses choices) or Age (which uses dates). They lack the dynamic date handling and predefined choice integration needed for these specific use cases.

Correct:

B. Information that will help better understand what drives quality and cost of Service Delivery This accurately reflects the needs of a Service Owner in Performance Analytics. Service Owners are responsible for the end-to-end delivery and performance of services, and they require insights into what factors influence service quality, efficiency, and cost. Performance Analytics provides them with KPIs, trends, breakdowns, and historical data that help identify bottlenecks, inefficiencies, and improvement opportunities across the service lifecycle. This enables data-driven decisions to enhance service value and customer satisfaction.

Incorrect:

A. Status and quality information about their submitted requests and the services they use This describes the needs of End Users, not Service Owners. End Users are primarily concerned with the progress and resolution of their own tickets or service requests, not the broader performance or cost drivers of the service.

C. Information around governance and high-level overview of process indicators to make better informed decisions This aligns more with the needs of Executives or Governance stakeholders, who focus on strategic oversight, compliance, and cross-process performance. While Service Owners may use some high-level indicators, their focus is more operational and service-specific.

D. Relevant targeted information that would help make the right decisions quickly and result in more efficient and better service This is more aligned with Operational Managers or Team Leads, who need actionable insights for day-to-day decisions. While Service Owners also make decisions, their scope is broader and more focused on service-level outcomes rather than immediate operational actions.

Correct:

B. Identifies the field that contains a unique value for every record in the Facts table This is the correct purpose of the Field attribute on a Breakdown Source form. In Performance Analytics, the Breakdown Source defines how data is segmented (e.g., by assignment group, location, priority). The Field attribute specifically identifies the column in the Facts table that holds the unique value used to associate each record with a breakdown element. This ensures accurate mapping between the indicator data and the breakdown values during data collection and visualization.

Incorrect:

A. Defines which Facts table field to use for mapping the Breakdown to Indicators While this sounds close, it‘s misleading. The Breakdown Mapping—not the Field attribute—is what links the Breakdown Source to an Indicator. The Field attribute only identifies the unique value within the Breakdown Source itself, not the mapping logic to indicators.

C. Provides security for the Breakdown values based on user permissions Security and access control for breakdown values are handled through Access Control Lists (ACLs) and role-based permissions, not through the Field attribute. The Field attribute has no role in enforcing visibility or security.

D. Limits the number of values returned by the Breakdown Source Limiting values is managed through filters or breakdown configurations, not the Field attribute. The Field simply identifies the unique value used for mapping, and does not control the volume or scope of returned data.

Correct:

C. pa_score_l1 This table stores Level 1 scores, which are the primary indicator scores collected during Performance Analytics jobs. These scores represent the aggregated values for indicators over time, such as daily or monthly counts, averages, or percentages. It is the main repository for time-series KPI data used in dashboards and widgets.

E. pa_score_l2 This table stores Level 2 scores, which are breakdown-specific scores. When an indicator is configured with breakdowns (e.g., by assignment group, location, or priority), the resulting segmented scores are saved in pa_score_l2. This enables detailed analysis and filtering of KPI data across different dimensions.

Incorrect:

A. pa_score This is a legacy or placeholder name and not the actual table used to store scores in current Performance Analytics implementations. The correct tables are pa_score_l1 and pa_score_l2, which handle base and breakdown scores respectively.

B. pa_drilldown_l1 This table is used for drilldown data, not for storing indicator scores. It supports the ability to view underlying records that contribute to a score, but it does not contain the score values themselves.

D. pa_snapshots_I1 Snapshots tables are used to store record-level snapshots of data at the time of collection, primarily for audit or historical reference. They do not store aggregated indicator scores and are not part of the core score storage mechanism.

F. pa_snapshots_I2 Similar to pa_snapshots_I1, this table holds breakdown-level snapshots of records, not scores. It supports detailed record tracking but is not used for storing KPI values.

Correct:

A. Who is the audience for this dashboard? Understanding the target audience is critical when designing a Performance Analytics dashboard. Whether it‘s executives, service owners, or end users, each group has different data needs, levels of detail, and preferred visualizations. Tailoring the dashboard to the audience ensures relevance, clarity, and usability.

B. Does the dashboard address key deliverables? A well-designed dashboard should align with business objectives and KPIs. It must clearly reflect the key deliverables or outcomes the organization is tracking—such as incident resolution time, SLA compliance, or service availability. This ensures the dashboard provides actionable insights tied to strategic goals.

E. Can actions be taken as a result of this dashboard? Dashboards should not just display data—they should enable decision-making. If users can interpret the data and take meaningful actions (e.g., reallocating resources, escalating issues), the dashboard is fulfilling its purpose. This is a core principle of effective Performance Analytics design.

Incorrect:

C. How wide should the dashboard be? While layout and responsiveness matter, dashboard width is a cosmetic or technical detail, not a strategic design consideration. The focus should be on content relevance, clarity, and user interaction—not screen dimensions.

D. In which ServiceNow release will be used? The ServiceNow release version is not a primary consideration during dashboard design. While some features may vary slightly between releases, the core design principles—audience, purpose, and actionability—remain consistent across versions.

Correct:

B. Lookup Lookup is not an available UI control type for Interactive Filters in Performance Analytics. Interactive Filters support specific UI controls designed for user-friendly selection and filtering of dashboard data, but Lookup is not among them. It may exist in other ServiceNow contexts (like form fields or reference selectors), but it is not supported as a control type for PA Interactive Filters.

Incorrect:

A. Radio Buttons Radio Buttons are a valid UI control type for Interactive Filters. They allow users to select a single value from a predefined set, making them ideal for filtering by discrete options like priority or category.

C. Select Multiple Input This is a supported control type that enables users to select multiple values simultaneously. It’s useful for filtering dashboards by multiple assignment groups, locations, or other multi-select fields.

D. Checkboxes Checkboxes are also a valid UI control type for Interactive Filters. Like Select Multiple Input, they allow users to choose multiple options, but with a different visual presentation. They’re commonly used for binary or categorical filters.

Correct:

A. report_admin This is the correct role for creating new Interactive Filters in Performance Analytics. The report_admin role has elevated permissions that allow users to configure and manage reporting components, including Interactive Filters, which are used to dynamically filter dashboard data. This role can define filter types, associate them with dashboards, and control their behavior across widgets.

Incorrect:

B. interactive_filter_user This role allows users to use Interactive Filters on dashboards but does not grant creation or configuration rights. It’s intended for consumers of dashboards who need to interact with filters, not for administrators or designers.

C. pa_admin While pa_admin has broad permissions over Performance Analytics components like indicators, breakdowns, and data collection jobs, it does not include rights to create Interactive Filters. Filter creation falls under reporting configuration, which is managed by report_admin.

D. pa_power_user This role provides access to view and interact with Performance Analytics dashboards and widgets, but does not allow creation of Interactive Filters. It’s designed for advanced users who consume analytics, not for those who configure reporting elements.

Correct:

D. Reporting Widgets Reporting Widgets are directly affected by Interactive Filters in Performance Analytics dashboards. These filters allow users to dynamically adjust the data displayed in reporting widgets based on selected criteria (e.g., date ranges, assignment groups, priorities). When an Interactive Filter is applied, the widget updates in real time to reflect the filtered dataset, making it a powerful tool for contextual analysis and decision-making.

Incorrect:

A. Text Analytics Widgets Text Analytics Widgets are used to visualize unstructured data insights (e.g., keyword frequency, sentiment analysis) and are not responsive to Interactive Filters. Their data is typically preprocessed and not dynamically filtered through dashboard controls.

B. List Widgets List Widgets display tabular data but do not respond to Interactive Filters. They are static representations of record lists and require manual configuration or scripted filtering, separate from the Interactive Filter framework.

C. Cascading Filter Widgets Cascading Filter Widgets are themselves a type of Interactive Filter, not a dashboard element that gets filtered. They are used to create dependent filter relationships (e.g., selecting a category narrows the available subcategories), but they do not display data and are not affected by other filters.

Correct:

D. By applying access control lists (ACLs) This is the correct and most effective method for enhancing security in Performance Analytics. Access Control Lists (ACLs) in ServiceNow define who can read, write, create, or delete records in specific tables, including those used by Performance Analytics (e.g., pa_score_l1, pa_indicator, pa_dashboards). By applying ACLs, administrators can restrict access to sensitive indicators, breakdowns, widgets, and dashboards, ensuring that only authorized users can view or manipulate PA data. This is a core security best practice for CAS–PA.

Incorrect:

A. By creating new roles While creating new roles can help organize permissions, it does not enforce security by itself. Roles must be paired with ACLs to have any effect. Simply creating a role without applying it to ACLs or assigning it to users does not enhance security.

B. By customizing existing roles Similar to creating new roles, customizing existing roles can help tailor access, but it is not sufficient on its own. Without properly defined ACLs that reference those roles, the system cannot enforce access restrictions. Roles are part of the security model, but ACLs are the enforcement mechanism.

C. By enabling new widgets Enabling widgets has no impact on security. Widgets are UI components used to display data, and enabling them only affects visibility or functionality—not access control. Security must be handled at the data and role level, not through widget availability.

Correct:

B. Data collection optimization properties The Data Collection log includes details about optimization settings used during the collection process. These properties help administrators understand how efficiently data is being collected, such as whether delta collection is enabled or if certain performance flags are active. This is crucial for troubleshooting and tuning collection jobs.

D. Domain for which scores are collected In domain-separated environments, the log reports the domain context under which scores are collected. This ensures transparency in multi-tenant setups and helps validate that data is being collected for the correct domain, especially in MSP or scoped deployments.

E. Breakdown exclusions The log captures Breakdown exclusions, which are configurations that prevent certain breakdown elements from being collected (e.g., inactive assignment groups). This helps administrators verify that the correct breakdown values are included or excluded during score generation.

Incorrect:

A. All Breakdowns being collected While breakdowns are part of the data collection configuration, the log does not list all breakdowns being collected in detail. It focuses on exclusions and mappings rather than providing a full enumeration of every breakdown element.

C. Data retention settings Data retention policies (e.g., how long scores are stored) are configured separately and are not reported in the Data Collection log. These settings are managed via system properties or table-level configurations, not logged during collection runs.

Correct:

C. Custom Business Calendar This is the correct calendar type used in Performance Analytics to analyze scores based on custom time periods. The Custom Business Calendar allows administrators to define non-standard time frames such as fiscal quarters, business weeks, or operational cycles that differ from the default Gregorian calendar. It enables tailored score analysis aligned with organizational reporting needs, making it essential for accurate KPI tracking in business-specific contexts.

Incorrect:

A. Team Calendar The Team Calendar is used for scheduling and availability of team members, not for Performance Analytics score analysis. It does not support time period definitions for indicator evaluation or dashboard filtering.

B. Maintenance Calendar This calendar is used to schedule planned maintenance windows for services or infrastructure. It is unrelated to score analysis and cannot be used to define time periods for Performance Analytics indicators.

D. On-Call Calendar The On-Call Calendar manages on-call rotations and shifts for support teams. While useful for operational scheduling, it does not integrate with Performance Analytics for score-based time period analysis.

Correct:

B. Scorecard options can be set to Key This is accurate. When the List Analytics widget is configured with Scorecard visualization, one of the available display options is “Key“, which highlights key indicators or breakdown elements. This helps focus the dashboard on critical metrics that require attention or represent strategic priorities.

E. Filter can be set to Deteriorated Also correct. The List Settings allow filtering based on performance trends, and “Deteriorated“ is a valid filter option. It enables users to isolate breakdown elements or indicators that have worsened over the selected time period, supporting root cause analysis and corrective action.

Incorrect:

A. Filter can be set to Best improved This is not an available filter option in Scorecard visualization settings. While users can filter by Improved, the specific term “Best improved“ is not a supported filter setting in the List Analytics widget configuration.

C. Page size can be set to 5, 10, 50 This is inaccurate. The Scorecard visualization does not support page size settings like traditional list views. It displays scorecards in a grid layout, and the number of items shown is controlled by widget size and layout—not by page size parameters.

D. Scorecard options can be set to Top 10 This is incorrect. “Top 10“ is a filtering or sorting concept, not a Scorecard option. Scorecard options refer to display styles like Key, All, or Improved/Deteriorated—not ranked subsets like Top 10.

Correct:

D. 2 > 3 > 1 > 4 > 5 This is the correct sequence for the Automated Indicator Guided Setup in Performance Analytics. Each step builds upon the previous to configure a fully functional indicator:

General – Define the indicator name, description, and basic properties.

Data Source – Select the source table and conditions that determine which records are evaluated.

Breakdowns – Choose breakdowns (e.g., by assignment group, priority) to segment the indicator data.

Data Collection – Configure how and when the indicator data is collected (e.g., daily, weekly).

Widget – Create a visual representation of the indicator for dashboards.

This flow ensures logical and technical dependencies are respected, aligning with CAS–PA best practices.

Incorrect:

A. 2 > 1 > 3 > 4 > 5 Incorrect because Breakdowns cannot be configured before the Data Source is defined. Breakdowns rely on the structure and fields of the selected data source.

B. 2 > 3 > 5 > 3 > 4 Invalid sequence with a repeated Data Source step and incorrect placement of Widget before Data Collection. You must collect data before visualizing it.

C. 2 > 3 > 4 > 1 > 5 Incorrect because Breakdowns must be configured before Data Collection. Breakdowns influence how data is segmented during collection, so placing them after collection is illogical.

Correct:

A. Four consecutive scores on the same side of the central line, with three of the scores close to the upper or lower limit This pattern is a classic indicator of short run signal behavior in Performance Analytics. It suggests a non-random variation that may be caused by a temporary shift or anomaly in the process. The proximity of three scores to the control limits increases the likelihood that the variation is significant and not due to normal fluctuation. Identifying short run signals helps ServiceNow administrators and analysts detect emerging issues before they become long-term trends.

Incorrect:

B. Seven consecutive scores on the same side of the central line This describes a long run signal, not a short run. A long run signal indicates a sustained shift in the process, possibly due to a systemic change. It’s used to detect persistent trends rather than short-term anomalies.

C. Every score beyond a three standard deviation (3-sigma) upper or lower limit This represents an outlier or special cause variation, not a short run signal. A single score beyond 3-sigma is typically considered statistically significant and may warrant immediate investigation, but it doesn’t reflect a short run pattern.

D. All scores inside two standard deviations This describes normal variation within control limits. It does not indicate any signal behavior—short or long run—and is expected in a stable process. No action is typically required unless other patterns emerge.

Correct:

A. Indicators The Explore and Manage section of the Admin Console provides access to Indicators, allowing administrators to view, search, and manage all configured Performance Analytics indicators. This includes automated, formula, and breakdown-enabled indicators, making it a central hub for indicator governance and maintenance.

B. Dashboard Groups Dashboard Groups are also accessible through Explore and Manage. These groups help organize dashboards into logical collections, improving navigation and access control. Admins can manage which dashboards belong to which groups and streamline user experience across roles and departments.

D. Reports Reports are included in the Explore and Manage section, enabling admins to review and manage saved reports that may be used in dashboards or standalone analysis. This includes tabular reports, visualizations, and report sources tied to Performance Analytics data.

Incorrect:

C. Targets Targets are configured separately under the Targets module or via the KPI Details interface. They are not part of the Explore and Manage section. While they relate to indicators, their configuration is handled through dedicated target management interfaces.

E. Current connected users Monitoring or viewing current connected users is not a Performance Analytics function and is not available in the Explore and Manage section. This type of system-level visibility is handled through platform monitoring tools or instance-level analytics, not PA configuration.

False

Users with the pa_power_user role cannot share dashboards, even if they can view them. This role is designed to allow users to consume and interact with Performance Analytics dashboards, including applying filters, drilling down into data, and viewing widgets. However, sharing dashboards—which involves granting access to other users or groups—is a privileged action reserved for roles like pa_admin or report_admin. Sharing requires elevated permissions to modify dashboard access controls, which pa_power_user does not possess.

Correct:

A. A null value This is the correct behavior. In Performance Analytics, when a Formula Indicator references a Manual Indicator that has monthly frequency and no score entered for the current month, the formula will treat the missing value as null. Manual Indicators require explicit score entry for each period, and if no score exists for the target date, the system does not auto-fill, forecast, or reuse previous values. As a result, the formula will compute using a null input, which may lead to a null or undefined result depending on the formula logic.

Incorrect:

B. The Forecasted value of the Manual Indicator Incorrect. Manual Indicators do not support forecasting. Forecasting applies to automated indicators with historical data trends. Manual Indicators rely solely on user-entered scores, so no forecasted value is available or used.

C. An error is generated Incorrect. The system does not throw an error when a Manual Indicator score is missing. Instead, it gracefully handles the absence by treating the value as null, allowing the formula to proceed (though the result may be null or blank).

D. The most recent value of the Manual Indicator Incorrect. Performance Analytics does not carry forward the last known value of a Manual Indicator. Each period must have its own explicitly entered score. Reusing prior values would compromise data integrity and misrepresent performance trends.

Correct:

C. A numeric value This is the correct output type when using a script to calculate the SUM aggregate of an Automated Indicator in Performance Analytics. The SUM operation totals the values of a specified field across all matching records in the indicator’s data source. Since it performs a mathematical aggregation, the result is a numeric value—such as a count, duration, or monetary amount—depending on the field being summed. This numeric output is then stored in the PA score tables and visualized in dashboards or widgets.

Incorrect:

A. The sys_ids of the matching elements Incorrect. The SUM aggregate does not return record identifiers like sys_id. It performs a calculation across records, not a selection or listing of them. If you needed sys_ids, you‘d use a drilldown or list widget—not a SUM aggregate.

B. The script generates no output Incorrect. A properly configured SUM script does generate output—specifically, a numeric score. If no output is generated, it likely indicates a misconfiguration or an empty result set, not the expected behavior.

D. A string value Incorrect. SUM is a numerical aggregation, not a text operation. It cannot return strings unless the script is incorrectly written or the field being summed is non-numeric, which would result in an error or null—not a valid string output.

Docs:
https://docs.servicenow.com/bundle/sandiego-now-intelligence/page/use/dashboards/task/convert-multiple-homepages-dashboards.html

Correct:

D. Create a conversion script and specify a Scripted Sum Aggregate This is the correct approach when you need to convert the output of a Formula Indicator—such as changing duration from milliseconds to hours—without creating a new indicator. In ServiceNow Performance Analytics, a Scripted Sum Aggregate allows you to apply custom logic during data collection or formula evaluation. You can write a script that divides the summed duration by 1000 * 60 * 60 to convert milliseconds to hours. This method ensures the transformation is applied consistently and efficiently, aligning with CAS–PA best practices.

Incorrect:

A. There is no way to convert the output of an Automated Indicator Incorrect. ServiceNow does support output conversion through scripted aggregates, formula manipulation, and unit settings. Saying there‘s no way to convert is factually wrong and ignores available configuration options.

B. Set the Unit to “Hours“ on the Indicator form Misleading. While you can label the unit as “Hours“ on the Indicator form, this does not perform any actual conversion. It only changes the display label, not the underlying data. The scores would still be in milliseconds unless explicitly transformed.

C. Add “/ (60“60 1000)“ to the Formula box and check “Use Formula“ Incorrect syntax and concept. The formula box in a Formula Indicator expects valid references and expressions. The string “/ (60“60 1000)“ is malformed and would cause a script error. Also, this method is not suitable for converting summed values unless handled through a scripted aggregate.

Correct:

D. Date Date fields are commonly used in Indicator Source conditions because Performance Analytics indicators often rely on time-based filtering to collect scores over specific periods (e.g., daily, weekly, monthly). For example, conditions like Resolved date is not empty or Created date is within last 30 days are typical in indicator sources. These date filters ensure that only relevant records are included in the score calculation for each time slice, making date fields essential for temporal analysis.

Incorrect:

A. Reference While reference fields (e.g., Assigned to, Category) are useful for breakdowns or grouping, they are not typically used as primary conditions in indicator sources. They help segment data but don’t drive time-based score collection.

B. True / False Boolean fields may be used occasionally (e.g., Active = true), but they are not the most common field type for indicator source conditions. They’re more useful for filtering static states rather than dynamic, time-based data.

C. String String fields (e.g., short descriptions, comments) are rarely used in indicator source conditions because they are unstructured and variable. They don’t lend themselves well to consistent filtering or aggregation, making them unsuitable for most PA indicator logic.

Correct:

A. For each widget, enable “Follow Interactive filter“ This is the correct configuration when you want a Report widget on a Performance Analytics dashboard to respond to Interactive Filters. By enabling “Follow Interactive filter“ in the widget settings, you allow the widget to dynamically update its data based on the filter selections made by the user. This ensures that the widget reflects only the relevant subset of data as defined by the active filters, which is essential for interactive, user-driven analysis.

Incorrect:

B. For each widget, enable “Act as Interactive filter“ This setting is used when you want a widget to function as an Interactive Filter itself, not to be filtered. It allows the widget to drive filtering behavior for other widgets, but it does not enable the widget to respond to filters.

C. Edit each widget and enable “Apply Dashboard Filter“ This is not a valid configuration option in the context of Performance Analytics dashboards. There is no setting labeled “Apply Dashboard Filter“ for Report widgets. The correct terminology and setting is “Follow Interactive filter.“

D. Edit each widget and enable “Follow element“ “Follow element“ is not a recognized configuration option for Report widgets in Performance Analytics. It may be confused with other platform features, but it is not related to enabling filter responsiveness.

Correct:

D. Create a Breakdown relation between the Assignment Group Manager and the Assignment Group breakdowns This is the correct and most efficient solution. In Performance Analytics, a Breakdown Relation allows you to define a hierarchical or logical connection between two breakdowns—here, between Assignment Group Manager and Assignment Group. When configured, this relation ensures that when a user drills into a manager element, the dashboard automatically filters to show only the Assignment Groups managed by that person. This satisfies the requirement without duplicating indicators or writing custom scripts, and aligns with best practices for scalable dashboard interactivity in CAS–PA.

Incorrect:

A. Modify the “Average age open incidents“ Indicator to add an additional condition that applied a dynamic filter, based on the logged in manager Incorrect. This approach would filter based on the logged-in user, not the selected breakdown element. It doesn’t support dynamic drilldown behavior tied to dashboard interaction, and would restrict visibility rather than enable exploratory filtering.

B. Create a separate Indicator for each manager and apply additional conditions to the Indicator to only include incidents where they are the Assignment Group manager Highly inefficient and incorrect. Creating multiple indicators for each manager is not scalable, violates PA design principles, and introduces maintenance overhead. It also doesn’t support dynamic drilldown behavior.

C. Create a script that runs against the Breakdown, where the elements are filtered to show only the manager‘s group Unnecessary and incorrect. While scripting could technically achieve this, it’s not the recommended method. Breakdown Relations are purpose-built for this use case and offer a declarative, maintainable solution without custom code.

Correct:

A. On the Indicator Source record This is the correct location to specify the maximum number of records that an Indicator Source can collect, overriding the default limit. In ServiceNow Performance Analytics, each Indicator Source has a field called “Maximum number of records“, which allows administrators to fine-tune data collection performance and scope. This setting ensures that the source does not exceed a defined threshold, which is especially useful when working with large datasets or optimizing collection jobs for speed and efficiency.

Incorrect:

B. On the related Indicators Incorrect. Indicators consume data from the Indicator Source, but they do not control how many records the source collects. The record limit must be set directly on the Indicator Source, not on the Indicator itself.

C. In the configuration of the collection job Incorrect. While collection jobs define when and how data is collected, they do not specify record limits for Indicator Sources. The job configuration handles scheduling and scope, but the record quantity is controlled at the source level.

D. Using a system property Incorrect. There is no system property that globally sets or overrides the record limit for Indicator Sources. This must be configured manually on each source record to ensure precise control.

Correct:

B. Act as interactive filter This is a valid customization option for Interactive Filter widgets. Enabling this setting allows the widget to serve as a filter source, meaning it can drive filtering behavior across other dashboard widgets. It’s essential for configuring widgets that control data views dynamically.

C. Set Header Color Also correct. You can customize the header color of an Interactive Filter widget to match dashboard themes or highlight important filters. This is part of the widget’s visual configuration settings and helps improve usability and design consistency.

D. Show Border/ Header/Title Correct. This option allows you to toggle the visibility of the widget’s border, header, and title. It’s useful for streamlining dashboard layout or emphasizing certain filters while minimizing visual clutter.

Incorrect:

A. Title vertical alignment Invalid. There is no supported customization for vertical alignment of the title in Interactive Filter widgets. Title positioning is handled automatically by the widget layout and cannot be manually adjusted vertically.

E. Follow interactive filter Incorrect in this context. “Follow interactive filter” is a setting used on Report or PA widgets, not on the Interactive Filter widget itself. It allows those widgets to respond to filters, but it is not a customization option for the filter widget.

Correct:

C. No notifications are sent, and no action is necessary This is the accurate interpretation of the ‘No signal‘ message in KPI Signals within Performance Analytics. It means that the system has not detected any statistically significant variation—such as short run, long run, or outlier behavior—in the indicator’s score pattern. As a result, no alerts or notifications are triggered, and no immediate action is required. This status reflects a stable KPI with no anomalies, aligning with CAS–PA best practices for signal monitoring.

Incorrect:

A. A workflow has changed or at least is not statistically stable Incorrect. The ‘No signal‘ message actually implies statistical stability, not instability. If the workflow had changed or the data showed instability, KPI Signals would likely detect a signal (e.g., short run or long run) and trigger an alert.

B. KPI Signals does not detect abnormal variation for a significant amount of time Misleading. While it’s true that no abnormal variation is detected, the duration is not the defining factor. The ‘No signal‘ message is based on current statistical analysis, not on how long the KPI has been stable.

D. There are seven consecutive scores above or below the central line Incorrect. This pattern represents a long run signal, which would trigger a signal notification. If such a pattern existed, the system would not display ‘No signal‘—it would classify it as a long run and alert accordingly.

? Correct Answer: B.
Table: Change Request, Conditions: Active is True, Related List Conditions: Greater than or Equal to 1 selected table records are related to a record on Incident ? Change Request, all of these conditions must be met Active is true

Correct:

A. Show score update time This is the correct setting that controls whether a timestamp is displayed on a Single Score visualization, indicating when the score was last updated. Enabling this option helps users understand the freshness of the data, which is especially important for time-sensitive KPIs. It adds a small timestamp below the score, improving transparency and trust in the dashboard‘s data.

Incorrect:

B. Background refresh interval (minutes) Incorrect. This setting determines how often the widget refreshes its data in the background, but it does not display any timestamp. It affects data polling frequency, not the visual display of update time.

C. Real time update Incorrect. This option enables live updates to the widget when underlying data changes, but it does not show a timestamp. It’s useful for dynamic dashboards but unrelated to displaying when the score was last updated.

D. Follow filters Incorrect. This setting allows the widget to respond to Interactive Filters on the dashboard. It controls data filtering behavior, not update visibility or timestamps.

Correct:

A. Usually a percentage Correct. Lagging indicators often represent summarized results, such as percentages (e.g., customer satisfaction rate, SLA compliance rate). These values reflect past performance and are commonly used to assess how well objectives were met.

C. Usually an average Correct. Lagging indicators frequently use averages to summarize historical data, such as average resolution time, average age of open incidents, or average handle time. These metrics help evaluate performance after the fact.

D. Measure outcomes Correct. This is a defining characteristic of lagging indicators—they measure the results or outcomes of processes or activities. For example, the number of closed incidents or customer satisfaction scores are outcomes that reflect what has already occurred.

Incorrect:

B. Drives outcomes Incorrect. This describes a leading indicator, not a lagging one. Leading indicators are predictive and help influence or anticipate future outcomes (e.g., number of open incidents, training hours completed). Lagging indicators, by contrast, do not drive outcomes—they measure them.

E. Easy to influence Incorrect. Lagging indicators are typically harder to influence directly, as they reflect past performance. They are the result of many contributing factors and actions that have already taken place, making them less actionable in real time compared to leading indicators.