AB-410 Exam Questions

Embedding models (like the Ada-002 or small-3) convert text into numerical vectors to enable semantic similarity searches.

The Retrieval Engine queries the vector store to find the most relevant context to include in the AI‘s prompt.

Hybrid search combines traditional keyword matching with modern vector search to provide the most accurate retrieval results.

Max Tokens sets a hard limit on the generated response length, effectively capping the cost and duration of the model‘s output.

Row-level security ensures the AI, acting for the user, only retrieves records that specific user has permission to see.

AI Enrichment in Azure AI Search uses skillsets to transform raw data into searchable insights during the ingestion phase.

A temperature of 0.0 makes the model deterministic, ensuring consistent and focused responses by selecting the most likely next token.

PII detection is a feature of Azure AI Language that identifies and masks sensitive data points within text.

Higher temperature results in more creative/random output; lower temperature (near 0) makes the model deterministic.

Reliability and Safety ensure that the application performs predictably and does not cause harm during edge-case scenarios.

Semantic Ranker re-evaluates the top search results using deep learning to ensure the most contextually relevant items are prioritized.

Zero-shot prompting relies on the model’s internal general knowledge to fulfill a request without specific guidance examples.

Azure Cost Management allows you to track spending and resource consumption at the service, resource group, or subscription level.

The Unstructured Document model uses layout-agnostic AI to find and extract data from free-flowing text documents.

Dynamic Chaining uses an orchestrator to determine which combination of tools and topics are needed for multi-step goals.

Azure AI Studio provides automated evaluation tools to calculate metrics like groundedness, coherence, and relevance.

Accountability ensures that there are clear lines of responsibility for the functioning and ethical outcomes of an AI system.

PII (Personally Identifiable Information) detection is a specialized feature of Azure AI Language designed to identify and mask sensitive data.

Azure AI Translator is the dedicated service for neural machine translation between dozens of global languages.

The Search Index is the specialized database that allows for high-speed retrieval of relevant context to ground the LLM.

Purview provides the governance framework to ensure data privacy and compliance within AI-driven systems.

Inclusiveness ensures that AI applications empower everyone and address the needs of diverse populations regardless of ability.

Azure AI Studio‘s Playground is a web-based environment for rapid experimentation with LLM configurations.

Global variables (or Bot variables) persist throughout the entire conversation, allowing information to flow between different intents.

Max Tokens determines the length of the generated output; for long summaries, this limit must be set high enough to allow the full text.

Grounding ensures that the AI’s responses are “anchored“ to your specific business data rather than general training knowledge.

Prompt Shields are part of Azure AI Content Safety, specifically designed to defend against jailbreaking and prompt injection.

Frequency Penalty discourages the model from repeating specific tokens, promoting more diverse and natural language.

Azure AI Studio provides the interface to configure and monitor content filtering policies for deployed models.

Azure OpenAI provides enterprise-grade security and compliance features for using large language models in a managed environment.

Entity Extraction is specifically built to pull structured data points from natural language text.

Max Tokens determines the length of the generated output; for long summaries, this limit must be set high enough to allow the full text.

Solutions are the standard mechanism used to bundle and move Power Platform assets through the Dev/Test/Prod pipeline.

Semantic Kernel is the SDK used to integrate Large Language Models with “Skills“ and “Connectors“ to build complex AI agents.

Augmentation is the step where the retrieved facts are added to the user‘s original query to create a context-rich prompt for the LLM.

Few-shot prompting uses examples to guide the model‘s logic and formatting, leading to more reliable outputs for specific tasks.

Evaluation tools in Azure AI Studio calculate metrics like groundedness, coherence, and relevance to assess model quality.

Azure AI Content Safety includes Prompt Shields to protect models from malicious instructions hidden in retrieved data.

This principle ensures the system is resilient and does not produce harmful or dangerous results during failures or edge cases.

Purview provides the governance framework to ensure data privacy and compliance within AI-driven systems.

The System Message defines the behavioral constraints, safety rules, and operational boundaries that the model must follow.

Prompt Flow in Azure AI Studio allows developers to create executable graphs linking LLMs, prompts, and Python code.

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

Unstructured models (layout-agnostic) use deep learning to find fields regardless of their physical position in the document.

Speech-to-Text is the core component that transcribes spoken language into a machine-readable format for the LLM.

The Unstructured Document model is designed for documents where the text and data points appear in varying locations.

PII (Personally Identifiable Information) detection identifies and masks sensitive data points within a text string.

Distillation creates more efficient, smaller models (SLMs) that retain the reasoning capabilities of larger “teacher“ models.

Vector search relies on the distance between high-dimensional numerical representations of text to find semantic matches.

Privacy and Security ensure the AI system protects data and is resilient against malicious manipulation and data leaks.

Dataverse Search provides intelligent, cross-table, and fuzzy matching capabilities essential for robust grounding.

Power Automate acts as the logic engine that allows an agent to perform multi-step actions across different connectors.

The Search Index is the optimized collection of data that the search engine queries to find relevant results quickly.

Accountability involves creating logs and feedback loops so that the system‘s impact can be analyzed and designers can be held responsible.

Prompt Shields are designed to identify and block malicious instructions embedded in user inputs or external data sources.

Rollup columns automatically calculate aggregates (like sum or count) from related records, providing real-time data for the AI.

Accuracy is a standard metric used to determine how often the model‘s predicted response matches the ground truth.

Private Endpoints allow you to access Azure AI services over a private IP address within your Virtual Network (VNet).

Reliability and Safety focus on ensuring the system handles errors gracefully and performs consistently under pressure.

Transparency involves being open about the AI‘s role, capabilities, and limitations to build and maintain user trust.