AB-410 Exam Questions

Accountability includes mechanisms for human feedback and oversight to help ensure the system operates correctly and safely.

Power Automate works as the logic engine that lets an agent carry out multi-step actions across different connectors.

The Unstructured Document model applies deep learning to detect text and fields even when layouts vary or image quality is imperfect.

Initial vector searches are fast but may lack precision; re-ranking uses a cross-encoder model to refine the relevance of the top results.

The GUID, or Globally Unique Identifier, is the unique ID for each Dataverse record, which helps the AI reference the exact intended row.

Trigger phrases are natural language examples that the orchestrator uses to match user intent to a specific topic.

Video Indexer automatically transcribes audio, detects speakers, and extracts key insights and timestamps from video content.

The System Message, or System Prompt, defines the AI‘s ground rules, such as requiring it to use only professional language.

Embeddings models represent the semantic meaning of text as numbers, allowing a vector database to find contextually relevant matches.

Azure OpenAI provides the same models as OpenAI, with additional Microsoft security, compliance, and data residency guarantees.

RAG combines a search index, which finds the specific paragraph, with an LLM, which drafts the natural language answer based on that paragraph.

Entities are used to identify specific information within natural language and save it for use later in the conversation.

Face Detection detects human faces and can also return attributes such as head pose or emotions.

Reducing temperature closer to 0 makes the model more deterministic and focused on the most likely response.

Field-level security lets administrators limit access to specific sensitive columns, even when the user can access the rest of the table.

This topic manages cases where the orchestrator cannot confidently match the user‘s message to an existing user-defined topic.

Document Translation is a specific feature for translating complete files while retaining the original formatting and structure.

Dataverse Search offers fast, intelligent search across multiple tables, which is needed to ground AI agents in Dataverse data.

The context window is the memory limit for a single prompt session; when it is exceeded, the model “forgets“ the start of the conversation.

The Prediction model evaluates historical patterns to forecast future outcomes, including a Yes/No event such as machine failure.

The Indexer is the engine that connects to data sources, such as Blob Storage or SQL, extracts the data, and writes it into the index.

Max Tokens limits the response length and helps control costs by stopping the model from generating excessively long outputs.

Using the JSON file as a Knowledge Source through RAG lets the bot retrieve current data at query time without the high cost of retraining or fine-tuning.

Microsoft Entra ID, formerly Azure AD, Managed Identities give apps a secure way to authenticate to services such as Azure OpenAI by using identity-based access.

Slot Filling is the process where a bot identifies and collects the specific information, or entities, needed to complete a task or answer a query.

Language Detection must be performed first so the source language is identified before the translation service processes the text.

The “Facetable“ attribute is used specifically to generate hit counts by category, which lets users drill down into search results.

A read-only, verified knowledge source keeps the model‘s “truth“ based on an authoritative database rather than untrusted user input.

Image Classification is used when you need to apply a label, such as a car brand, to an entire image.

The System Message sets the persona and stylistic constraints that the model must follow during the conversation.

Semantic Kernel is the SDK developers use to orchestrate AI services with traditional programming languages.

OAuth2 with Entra ID supports delegated permissions, so the bot can securely access data that is specific to the user.

Azure AI Vision‘s spatial analysis and object detection features are used to identify specific objects such as Personal Protective Equipment (PPE).

Semantic ranking uses language models to interpret the intent of a query, helping the most relevant results appear first.

Chunking is needed because LLMs have context window limits; smaller segments help retrieve the most relevant information.

Azure AI Health Insights, specifically Health Text Analytics, is optimized to identify and structure complex medical entities.

Image Classification categorizes an entire image, or a specific area, into predefined labels such as “Empty“ or “Full.“

Conversation Summarization is tuned specifically to extract key points and resolutions from multi-turn dialogues.

Cross-lingual embeddings place different languages in the same vector space, so the system can identify that “Apple“ in English and “Manzana“ in Spanish are semantically similar.

Variables serve as the bot‘s short-term memory, helping it retain details the user provided earlier in the session.

The Prebuilt Receipt model is trained specifically to detect key-value pairs commonly present in financial receipts and invoices.

Custom Vision lets the company train a model to recognize specific types of vehicle damage and estimate how severe they are.

Plugins in Copilot Studio let the agent take actions, such as starting an automated workflow based on the conversation context.

Generative Answers let the bot “boost“ its knowledge by searching designated websites or SharePoint sites in real time.

Custom Connectors let Power Platform tools securely communicate with external REST APIs or databases.

Azure AI Document Intelligence provides prebuilt and custom models for extracting specific fields and structures from documents.

Semantic Ranking greatly improves search relevance by understanding the intent and context behind a search query.

Few-shot learning uses in-context examples to help the model understand the required format or logic for a task.

Azure AI Search provides vector indexing and search, which are required to match user queries with the most relevant data chunks.

Microsoft Purview provides data governance and auditing capabilities that track how sensitive data is used in AI applications.

The Prebuilt Invoice Processing model is specifically trained to identify and extract common financial document fields, such as totals, dates, and vendor names.

The Speech Translation feature in Azure AI Speech can take in audio and return both text-based and speech-based translation in near real time.

Zero-shot prompting depends on the model‘s pre-trained knowledge to complete a request without receiving specific examples in the prompt.

A higher temperature produces more creative or random output, while a lower temperature, near 0, makes the model more deterministic.

The Retrieval Engine searches the vector index to locate the data chunks that are most semantically similar for inclusion in the AI‘s prompt context.

Slot filling captures the entity value, such as the seat type, from the user‘s natural language and stores it in a variable.

Video Indexer detects faces and provides a timeline showing each point where a specific person appears during the video.

Chain-of-Thought, or CoT, prompting improves performance on complex tasks by making the model state the logic that leads to its conclusion.

In AI Builder, Category Classification is built to assign text to predefined categories by using historical examples.

Top P, also called Nucleus Sampling, is an alternative to Temperature that manages output diversity by narrowing the pool of possible word choices.