AI-103 Implement Generative AI And Agentic Solutions Practice Test 2 — 15 Questions

Question 1

Which TWO features in Azure AI Foundry help you manage the lifecycle of your AI agents?

Accepted Answer

Model deployment versions.. Model deployment versions (A) are correct because Azure AI Foundry lets you create and manage multiple versions of a deployed model, enabling you to roll forward or roll back the model backing an agent as part of its lifecycle management. Prompt Flow evaluation runs (C) are correct because they let you systematically evaluate an agent's flow against datasets and metrics, producing run records that support iterative development, quality gating, and promotion decisions across the agent lifecycle. The Azure OpenAI Playground history (B) is not a lifecycle management feature; it is only a record of interactive test sessions and does not version, evaluate, or govern agents. Azure AI Content Safety logs (D) are for monitoring harmful content detections, not for managing agent lifecycle stages. The global search index (E) is a discovery/search capability and has no role in versioning, evaluating, or promoting agents.

Answer

The Azure OpenAI Playground history.

Answer

Azure AI Content Safety logs.

Answer

The global search index.

Question 2

Which TWO resources are required to set up an Azure AI Foundry project?

Accepted Answer

An Azure AI Services or Azure OpenAI resource.. Option D is correct because an Azure AI Foundry project must be linked to an Azure AI Services (Cognitive Services) resource or an Azure OpenAI resource, which supplies the model endpoints, keys, and quota used by the project's hubs and deployments. Option E is correct because a storage account (Azure Blob Storage) is a required connected resource that Foundry uses to persist project artifacts, datasets, and evaluation outputs. Option A is not required, since Azure Data Factory is an optional data-integration service that is not part of the Foundry project provisioning requirements. Option B is not required, because Foundry is a fully managed platform-as-a-service and does not need a user-provisioned virtual machine. Option C is not required, because Foundry abstracts the underlying compute and does not require a dedicated Kubernetes cluster.

Answer

An Azure Data Factory instance.

Answer

A virtual machine.

Answer

A dedicated Kubernetes cluster.

Question 3

Which THREE metrics should you monitor to evaluate the effectiveness of a RAG-based agent?

Accepted Answer

Retrieval Precision (the accuracy of the retrieved documents).. Retrieval Precision (A) is correct because in a RAG pipeline the retriever's job is to surface the most relevant documents, and precision directly measures how many of the retrieved chunks are actually relevant, which determines whether the generator receives useful grounding context. Answer Relevance (B) is correct because it evaluates the final generated response against the user's query, capturing whether the agent actually addressed the question rather than producing off-topic or hallucinated content. Model Latency, specifically time to first token (E), is correct because it is a key effectiveness/performance metric for a RAG agent, reflecting retrieval plus generation responsiveness and directly affecting user experience. Model Parameter count (C) is not a runtime effectiveness metric — it is a static architectural property of the underlying LLM and does not measure RAG quality. The number of times the model was restarted (D) is an operational availability statistic, not a measure of retrieval or answer quality, so it does not belong.

Answer

Model Parameter count.

Answer

The number of times the model was restarted.

Question 4

You are troubleshooting a Prompt Flow and suspect the issue lies in the input processing. Which THREE tools or techniques can you use?

Accepted Answer

Setting breakpoints in the visual designer.. Option A is correct because Prompt Flow's visual designer supports setting breakpoints on individual nodes, letting you pause execution and inspect the exact input payload entering the input-processing step. Option B is correct because the Trace feature records the inputs and outputs of every node in a flow run, so you can pinpoint where the input processing diverges from expectations. Option C is correct because execution logs capture runtime diagnostics, errors, and intermediate values that reveal what happened during input processing. Option D is not appropriate because deleting and recreating the resource group is a destructive, unrelated action that would not diagnose input processing and could cause data loss. Option E is not appropriate because raising the model temperature only affects the LLM's sampling randomness and does not help inspect or validate the input-processing logic.

Answer

Deleting and recreating the entire resource group.

Answer

Increasing the model temperature to see if the error persists.

Question 5

Which THREE actions occur when you 'deploy' an agent from Azure AI Foundry?

Accepted Answer

The agent's configuration is persisted as a versioned deployment.. Deploying an agent in Azure AI Foundry persists the agent's configuration as a versioned deployment (B), which is the core purpose of the deploy action—it snapshots the agent definition (model, instructions, tools, etc.) into an immutable, addressable version. The deployment also generates an HTTP endpoint (D) that client applications can call to invoke the agent, enabling integration via REST/SDK. Additionally, deployment allocates capacity and quota (E) for the underlying model, since the agent's model must have provisioned throughput or token quota to serve requests. Options A and C are incorrect: deployment does not retrain the model on new data (A), and it does not delete prior chat history (C)—conversation state is managed separately via threads and is unaffected by deploying a new agent version.

Answer

The model is automatically retrained on new data.

Answer

All previous chat history is deleted from the server.

Question 6

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 (Tools) in Azure AI Agent Service allows the agent to invoke external APIs by defining tool schemas that describe the API's parameters and expected responses. When a customer asks about stock levels, the agent emits a structured function call that the runtime executes against the inventory REST API, then feeds the result back into the model to formulate a natural-language answer. This is the purpose-built mechanism for connecting agents to external systems.

Answer

Prompt shields

Answer

Azure AI Content Safety filters

Answer

Azure Cognitive Search index

Question 7

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.. Enabling semantic ranking and using hybrid search (combining vector embeddings and BM25 keyword search) improves retrieval accuracy by leveraging both semantic understanding and keyword matching. This addresses the issue of conversational queries missing exact keywords.

Answer

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

Answer

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

Answer

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

Question 8

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 integrated into the model gateway inspects incoming prompts for harmful content, jailbreak attempts, and restricted topics before they reach the model, providing a centralized safety layer for multi-agent orchestration. This is the correct component for pre-model prompt evaluation and filtering.

Answer

Azure Key Vault access policies

Answer

Azure Cognitive Search semantic ranker

Answer

Azure API Management rate limiting policy

Question 9

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. Agentic workflows in Azure AI Foundry (and similar frameworks like Semantic Kernel or AutoGen) require a bounded orchestration loop; configuring a maximum iteration or step limit causes the orchestrator to terminate or escalate when the agent exceeds the threshold, preventing infinite tool-calling loops. This is the standard architectural safeguard for multi-step reasoning agents. It directly addresses the symptom of repeated identical tool calls.

Answer

Increase the vector store chunk overlap size

Answer

Enable Azure API Management caching for the tool endpoint

Answer

Set the model frequency penalty to 2.0

Question 10

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. Document-level access control in Azure AI Search is implemented by storing security identifiers (user or group IDs) in document metadata fields and applying OData filter expressions at query time to restrict results to documents the caller is authorized to see. The application passes the user's group memberships as filter parameters, and Azure AI Search returns only matching documents. This enforces security trimming at the retrieval layer, which is essential for RAG scenarios where the LLM must not receive unauthorized content.

Answer

Configure Microsoft Entra ID authentication on the Azure OpenAI endpoint

Answer

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

Answer

Use Azure AI Content Safety to redact sensitive keywords before indexing

Question 11

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. Explicit grounding instructions in the system prompt — such as 'Answer only using the provided context; if the context does not contain the answer, respond that you do not know' — are the standard prompt-engineering technique to constrain the model to retrieved context and refuse out-of-scope questions. This is a negative constraint that directly shapes model behavior at inference time. It is the correct, low-cost, deterministic approach for RAG grounding.

Answer

Configure Azure AI Content Safety severity levels to high

Answer

Set the model temperature to 1.0 and top_p to 0.0

Answer

Enable semantic caching on the Azure OpenAI deployment

Question 12

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. Azure AI Document Intelligence with layout extraction preserves table structure by detecting rows, columns, and cells during parsing, so tables are chunked as coherent units rather than split arbitrarily. This keeps numerical relationships intact and prevents the retrieval step from returning partial tables that omit key figures. It is the recommended remediation for table-splitting problems in RAG ingestion.

Answer

Increase the embedding model dimensions from 1536 to 3072

Answer

Set the Azure OpenAI model max_tokens parameter to 4096

Answer

Apply Azure AI Content Safety image analysis on all PDF pages

Question 13

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. Model evaluation in Azure AI Foundry provides a built-in evaluation framework where you can run your deployed model against a validation dataset and compute metrics like groundedness, relevance, fluency, coherence, and safety metrics (hate, violence, sexual, self-harm). It is the native tool for pre-production performance and safety assessment. Azure Monitor and Advisor serve different purposes (observability and cost), and API Management is for API governance.

Answer

Azure Monitor Log Analytics query packs

Answer

Azure Advisor cost recommendations

Answer

Azure API Management developer portal

Question 14

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. Semantic Kernel and Microsoft AutoGen are Microsoft's frameworks designed for building AI agents and multi-agent orchestration, including patterns where an orchestrator delegates to specialized worker agents. They provide abstractions for agent roles, conversation routing, and tool use that map directly to the described workflow.

Answer

Azure Data Factory pipeline authoring

Answer

Azure Stream Analytics query editor

Answer

Azure Logic Apps standard workflow designer

Question 15

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 is specifically designed to detect both direct (user) and indirect (document/email-embedded) prompt injection attacks. For an agent that reads emails, indirect prompt injection is the threat model — malicious instructions hidden in email content — and Prompt Shields analyzes the content to flag it before the agent acts on it. This is the only option that addresses prompt injection at the AI layer.

Answer

Azure DDoS Protection standard tier

Answer

Azure Web Application Firewall (WAF) SQL injection rules

Answer

Microsoft Defender for Cloud threat detection