AI-103 Implement Generative AI And Agentic Solutions — 25 Questions

Question 1

You are implementing Retrieval-Augmented Generation (RAG) using Azure AI Search and Azure OpenAI. Users report that when searching for technical product specifications, the system occasionally retrieves outdated product manuals because newer versions share similar titles. What configuration change should you make to improve retrieval accuracy?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable semantic search and semantic ranking on the Azure AI Search index. Enabling semantic search and semantic ranking in Azure AI Search allows the system to understand user intent and rank results based on deep semantic meaning rather than just keyword matches.

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Increase the chunk size parameter in the document ingestion pipeline to 4000 tokens

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Decrease the temperature parameter of the Azure OpenAI generation model to 0.0

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Switch the embedding model from text-embedding-ada-002 to text-embedding-3-small

Question 2

You are configuring an Azure OpenAI Service deployment in Microsoft AI Foundry portal. You need to ensure that the deployed model meets your enterprise compliance requirements for data privacy, ensuring that prompt and completion data are not used to train Microsoft base models. Which deployment type should you select?

Accepted Answer

Global Standard deployment. Standard deployments in Azure OpenAI Service ensure that customer data is not used to train Microsoft base models. Data-zone residency applies by default to standard deployments in supported regions.

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Classic Azure OpenAI fine-tuned model deployment

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Data Zone Standard deployment with model tuning enabled

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Provisioned Throughput deployment

Question 3

You are building a customer service agent in Azure AI Foundry. The agent needs to call an external inventory management REST API when customers ask about stock levels. Which feature of Azure AI Agent Service should you implement to allow the agent to interact with this external API?

Accepted Answer

Function calling (Tools). Function calling (or tool definitions) allows agents to invoke external APIs and custom functions by generating structured JSON payloads based on user intents.

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Prompt shields

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Azure AI Content Safety filters

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Azure Cognitive Search index

Question 4

You are designing a RAG solution using Azure AI Search and Azure OpenAI. Users report that when querying technical documentation, the system misses highly relevant paragraphs because the exact keywords do not match the user's conversational query. How should you configure Azure AI Search to improve retrieval accuracy?

Accepted Answer

Enable semantic ranking on the Azure AI Search index and use hybrid search combining vector embeddings and keyword BM25.. Semantic ranking in Azure AI Search applies advanced machine learning models to understand the intent of the query and re-rank search results based on semantic relevance rather than pure keyword matching.

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Switch the index analyzer to a standard whitespace analyzer to prevent stemming issues.

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Increase the number of retrieved documents (top_k) from 3 to 20 without changing the query type.

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Convert all documents to uppercase before indexing to ensure case-insensitive matching.

Question 5

You are building an agentic workflow in Microsoft Foundry using Semantic Kernel. The agent needs to call an external enterprise API to retrieve customer billing data. The API requires OAuth 2.0 user-delegated tokens. How should you configure the plugin registration to securely pass the user context?

Accepted Answer

Configure the Semantic Kernel plugin using an OpenApiFunctionDefinition that accepts a dynamic context parameter mapped to the incoming authorization header.. User-delegated authentication in Semantic Kernel plugins requires leveraging the active turn context to propagate the user's OAuth 2.0 token through the kernel invocation pipeline.

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Disable authentication on the external API and rely solely on network-level Azure VNet integration.

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Store the user's password in Azure Key Vault and have the plugin retrieve it on every tool call.

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Hardcode the service account client secret inside the plugin configuration settings.

Question 6

You are developing a multi-agent orchestration solution using Azure AI Agent Service and want to ensure that user inputs are safely evaluated before they are passed to the model. Which component should you configure to inspect and filter incoming prompts for malicious content and restricted topics?

Accepted Answer

Azure AI Content Safety integrated into the model gateway. Azure AI Content Safety is integrated into Azure AI Foundry and agent services to inspect prompts and completions for safety violations, jailbreak attempts, and restricted topics before they reach the model.

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Azure Key Vault access policies

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Azure Cognitive Search semantic ranker

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Azure API Management rate limiting policy

Question 7

You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?

Accepted Answer

Configure a maximum iteration step limit in the agent orchestration loop. Implementing agent execution limits, such as maximum iteration counters or step limits within the orchestration loop, prevents agents from falling into infinite tool-calling loops.

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Increase the vector store chunk overlap size

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Enable Azure API Management caching for the tool endpoint

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Set the model frequency penalty to 2.0

Question 8

You are designing a RAG solution in Azure AI Foundry where sensitive enterprise documents must be retrieved. Users should only see search results corresponding to their security clearance level. How should you implement document-level access control in Azure AI Search?

Accepted Answer

Store user security groups in document metadata and apply OData filter expressions during search queries. Security trimming in Azure AI Search is implemented by adding user security identifiers (IDs or group memberships) as metadata fields on each document and applying OData filter expressions at query time.

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Configure Microsoft Entra ID authentication on the Azure OpenAI endpoint

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Encrypt the search index using customer-managed keys (CMK) in Azure Key Vault

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Use Azure AI Content Safety to redact sensitive keywords before indexing

Question 9

You are building an agent using Azure AI Agent Service that needs to maintain long-term conversational memory across multiple sessions with a user. Which feature should you utilize to store and retrieve past user interactions?

Accepted Answer

Agent threads and message history APIs. Azure AI Agent Service provides built-in thread management where conversation histories are stored and persisted across sessions.

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Azure Cache for Redis with session state middleware

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Azure Cosmos DB vector search collections

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Azure AI Search index vector store

Question 10

You are writing a system prompt for an Azure OpenAI model deployed in Azure AI Foundry. You want to ensure the model responds only using the provided context and refuses to answer if the context is insufficient. What technique should you use?

Accepted Answer

Include explicit grounding instructions and a negative constraint in the system prompt. Grounding instructions combined with explicit refusal behavior in system prompts establish strict boundaries for RAG applications.

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Configure Azure AI Content Safety severity levels to high

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Set the model temperature to 1.0 and top_p to 0.0

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Enable semantic caching on the Azure OpenAI deployment

Question 11

You are optimizing a RAG pipeline in Azure AI Foundry. Users complain that answers are missing key numerical details because important tables within PDF documents are being split across chunk boundaries during ingestion. What is the recommended remediation?

Accepted Answer

Use Azure AI Document Intelligence with layout extraction to parse tables and maintain table structure during chunking. Using specialized document intelligence layout models or hierarchical chunking strategies ensures tables are parsed as coherent structures and kept intact within single chunks.

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Increase the embedding model dimensions from 1536 to 3072

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Set the Azure OpenAI model max_tokens parameter to 4096

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Apply Azure AI Content Safety image analysis on all PDF pages

Question 12

You are deploying an Azure OpenAI model in Azure AI Foundry and want to evaluate its performance and safety against a validation dataset before moving to production. Which tool should you use?

Accepted Answer

Model evaluation in Azure AI Foundry. Azure AI Foundry evaluation tools allow developers to run automated evaluations using metrics like groundedness, relevance, and coherence against test datasets.

Answer

Azure Monitor Log Analytics query packs

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Azure Advisor cost recommendations

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Azure API Management developer portal

Question 13

You are designing an agentic workflow where an orchestrator agent delegates sub-tasks to specialized worker agents (e.g., a research agent and a coding agent). Which framework or service native to Azure AI supports multi-agent orchestration patterns?

Accepted Answer

Semantic Kernel or Microsoft AutoGen. Microsoft AutoGen and Semantic Kernel are core frameworks supported within Azure AI for multi-agent orchestration and collaborative agent workflows.

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Azure Data Factory pipeline authoring

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Azure Stream Analytics query editor

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Azure Logic Apps standard workflow designer

Question 14

You are implementing indirect prompt injection protection for an agent that reads emails and summarizes them. An incoming email contains malicious instructions designed to trick the agent into exfiltrating user data. Which Azure AI feature should you configure to detect this threat?

Accepted Answer

Prompt Shields for indirect prompt injection in Azure AI Content Safety. Prompt Shields in Azure AI Content Safety include detection for indirect prompt injections, identifying when untrusted data sources attempt to hijack agent behavior.

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Azure DDoS Protection standard tier

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Azure Web Application Firewall (WAF) SQL injection rules

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Microsoft Defender for Cloud threat detection

Question 15

You are configuring a connection between Azure AI Foundry and an Azure OpenAI resource. Which credential type is recommended for secure, keyless authentication between services in Azure?

Accepted Answer

Managed Identity with Azure RBAC. Managed identities (system-assigned or user-assigned) provide secure, keyless authentication between Azure resources without storing credentials in code or configuration files.

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Account primary access key stored in application configuration

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Hardcoded personal access tokens (PAT)

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Anonymous public access with IP whitelisting

Question 16

You are building a RAG application where documents are updated frequently. You need to ensure that the Azure AI Search index reflects document deletions and updates in near real-time without performing full re-indexing. How should you design the ingestion pipeline?

Accepted Answer

Configure Azure AI Search indexers with change tracking and scheduled incremental synchronization. Using incremental indexers with change tracking (such as integrated data change detection or custom push APIs with soft-delete metadata) allows efficient updates.

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Increase the Azure OpenAI model deployment concurrency quota

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Enable semantic caching on the OpenAI endpoint

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Delete the entire search service and recreate the index daily using a batch script

Question 17

You are developing a generative AI application that uses Azure OpenAI. You want to track token consumption per user department for chargeback purposes. Which feature should you enable and utilize?

Accepted Answer

Azure Monitor metrics and diagnostic logs with custom metadata tags. Enabling Azure OpenAI usage metrics combined with custom headers or Microsoft Entra ID claims allows tracking and auditing token consumption by department.

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Azure Cost Management budgeting alerts only

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Azure AI Content Safety severity reports

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Azure Advisor security recommendations

Question 18

You are designing a generative AI solution in Azure AI Foundry. You need to ensure that user data sent to Azure OpenAI is not used to train Microsoft's foundational models. What is Microsoft's default data privacy policy for Azure OpenAI?

Accepted Answer

Customer data is not used to train foundation models by default. Microsoft guarantees that customer data and prompts sent to Azure OpenAI are not used to train or improve foundation models.

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Customer data is stored in public Azure storage accounts

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Customer data is automatically published to public hugging face repositories

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Customer data is used for model training unless you opt out by submitting a support ticket

Question 19

You are tuning the prompt for a generative AI application in Azure AI Foundry. The model frequently hallucinates when asked complex multi-part questions. You decide to use chain-of-thought (CoT) prompting. How should you structure the prompt to implement CoT effectively?

Accepted Answer

Instruct the model to think step-by-step and show its intermediate reasoning before providing the final answer. Chain-of-thought prompting involves guiding the model to articulate intermediate reasoning steps before arriving at the final conclusion.

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Set the model seed parameter to a fixed integer value for reproducibility

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Reduce the maximum completion token limit to 150 tokens

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Instruct the model to output its response exclusively in JSON format with strict schema validation

Question 20

You are building an advanced agentic RAG solution. When a user asks a complex question, the agent needs to generate multiple search queries, execute them in parallel against Azure AI Search, synthesize the findings, and then formulate a final answer. Which agent pattern are you implementing?

Accepted Answer

Query decomposition and parallel retrieval. Query decomposition and parallel retrieval is an advanced RAG pattern where complex queries are broken into sub-queries and executed concurrently.

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Model fine-tuning

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Single-shot keyword extraction

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Static prompt templating