AB-410 Exam Questions

Prompt Shields are a specialized defense in Content Safety designed to catch adversarial inputs.

Purview provides data lineage, which is essential for auditing the source and flow of data into AI models.

Language Detection identifies the source language, enabling the bot to switch to the appropriate localized response logic.

Content Safety includes Prompt Shields, which are specifically designed to block jailbreak and prompt injection attempts.

Microsoft Purview‘s AI Hub provides centralized visibility into AI usage, risks, and compliance across the organization.

Regularly testing the model against a “Golden Dataset“ of current data ensures that accuracy remains high as trends evolve.

Metadata filtering allows the system to ignore irrelevant data based on structured attributes before performing the semantic search.

System topics are the foundational logic blocks built into every bot to handle common conversational scenarios.

Handoff ensures that complex or high-touch queries are seamlessly transitioned to the appropriate resource.

Content Safety for Images detects harmful visual content across several categories (Hate, Self-harm, Sexual, Violence).

Document Intelligence uses advanced deep learning to handle complex OCR tasks, including distorted or high-noise images.

Model Versioning allows developers to control when they upgrade to newer model releases, ensuring stability for their apps.

Ownership-based security allows for granular control based on who created or owns a specific record.

Purview tracks data lineage, showing the journey of data from its source to its use in AI models.

The Evaluation tool in AI Studio measures “Groundedness“ to see if the AI is making up information.

Few-shot prompting uses a small number of examples to “guide“ the model toward the correct output format or reasoning logic.

Prompt Shields (part of Content Safety) detect and block malicious instructions hidden in retrieved data or user prompts.

Fairness focuses on ensuring that AI systems treat all groups of people equitably and do not perpetuate social bias.

Fairness focuses on the equitable treatment of all users and the mitigation of historical or systemic biases in data.

Solutions are the ALM mechanism used to bundle apps, flows, and bots together for secure and consistent deployment.

Microsoft Purview AI Hub provides centralized visibility into AI usage and data security.

Microsoft Purview AI Hub provides visibility into how AI is being used, ensuring data security and compliance.

Phi-3 is an efficient SLM designed to provide high reasoning capabilities in a smaller, faster, and more cost-effective package.

Metadata filtering applies hard filters (like Date or Category) before the semantic search to improve accuracy.

Dynamic Chaining allows the system to build a custom “chain“ of actions/agents on the fly based on the user‘s specific request.

Azure AI Studio‘s evaluation tools assess responses for safety risks, such as hate speech or self-harm, providing a quantifiable score.

RAG allows the agent to retrieve information from a dynamic, external knowledge base to ground its responses.

“Computer Use“ allows agents to interact with graphical user interfaces (browsers/apps) just like a human operator.

The Speech service handles live audio-to-text and audio-to-audio translation (Speech Translation) with low latency.

Entity Extraction is specifically built to pull structured data points (like currency or dates) from natural language text.

A Temperature of 0 makes the model deterministic, ensuring the same input always yields the same output.

Self-correction allows the agent to iterate on its own reasoning to ensure it meets constraints or factual requirements.

Accountability involves creating mechanisms for human oversight and feedback to ensure the system operates correctly.

Document Intelligence (formerly Form Recognizer) is optimized for high-accuracy OCR, even with challenging or low-quality images.

“Computer Use“ is an agentic feature that allows the bot to interact with non-API-based web interfaces and desktop apps like a human.

Slot filling captures the entity value from natural language and saves it into a variable for logic processing.

TTFT is a critical performance metric for real-time applications, measuring how “snappy“ the bot feels to the end user.

Privacy and Security ensure that personal and sensitive data are handled in compliance with privacy laws and protected from leaks.

Content filtering ensures the AI adheres to safety standards by blocking unsafe inputs and outputs.

The Retrieval Engine queries the index to find the most semantically similar context to include in the prompt.

Transparency involves making the AI‘s “thought process“ and data sources clear to the end user.

Purview provides the data governance framework to ensure that AI models are using compliant and authorized data sources.

Azure AI Studio‘s Evaluation tab provides metrics like groundedness and relevance to assess model quality against datasets.

Entities allow the bot to identify and “fill“ specific data slots (like a Date or Name) within a sentence.

Contextual chunking ensures related ideas (like a whole paragraph) stay together, improving the quality of retrieved context.

Kernel functions are the fundamental building blocks (skills/logic) that the agent uses to perform tasks or call LLMs.

Dataverse Search (powered by Azure AI Search technology) provides a global, intelligent search experience across all relevant tables.

Smaller chunks with overlap ensure that fine-grained details aren‘t lost at the boundaries of the text segments during retrieval.

Increasing the Temperature adds more randomness to the token selection, resulting in more creative or varied outputs.

MCP is a standard used within the Copilot ecosystem to enable agents to safely bridge the gap between AI models and local/remote data.

Integrating Azure AI Speech with Conversational Language Understanding (CLU) allows the system to map audio directly to business intents.

The Face API provides face detection and 1:1 verification matching.

The Prebuilt Receipt model is specifically trained to identify and extract key-value pairs from financial receipts.

Video Indexer identifies objects, people, and themes across the timeline of a video, making visual content searchable.

Agentic “Computer Use“ allows the AI to perceive the screen and interact with UI elements like a human would.

Document Intelligence is specifically optimized for high-accuracy OCR in challenging or unstructured visual conditions.

Brand Detection in Azure AI Video Indexer identifies thousands of world-famous logos and brands within video frames.

SLMs are efficient, fast, and can often be deployed locally or at the edge for specific reasoning tasks.

Frequency Penalty specifically targets verbatim repetition of tokens to make the output more natural.

Agentic AI goes beyond conversation; it uses reasoning to utilize tools, navigate apps, and complete workflows independently.