AI-103 Implement Generative AI And Agentic Solutions Practice Test 4 — 15 Questions

Question 1

You are building a chat application with Azure OpenAI. To improve perceived latency for users, you want to stream the model's response token-by-token to the client interface. Which API parameter or client SDK feature should you enable?

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

Accepted Answer

Enable streaming in the chat completion request (stream=True). Setting stream=True in the chat completion request enables server-sent events (SSE) that deliver tokens incrementally as the model generates them, dramatically improving perceived latency. The client SDKs expose this as a streaming iterator or callback, letting the UI render partial responses immediately.

Answer

Set max_tokens to 1

Answer

Configure Azure AI Search with hybrid search

Answer

Enable semantic caching

Question 2

You are designing an AI application and want to test different foundation models (e.g., GPT-4o vs. Llama 3) side-by-side using the same prompt and test dataset. Which feature in Azure AI Foundry should you use?

Accepted Answer

Side-by-side evaluation in Azure AI Foundry playground. Side-by-side evaluation in the Azure AI Foundry playground lets you run the same prompt and test dataset against multiple foundation models and compare outputs, metrics, and quality. This is the native feature for model comparison during design and selection.

Answer

Azure Monitor workbook performance graphs

Answer

Azure Cosmos DB cross-region replication

Answer

Azure Front Door traffic routing

Question 3

You are developing a RAG solution using Azure AI Search. Users are searching using domain-specific acronyms that are not well-represented in standard embedding models, resulting in poor retrieval scores. What feature should you enable in Azure AI Search to improve keyword matching for exact acronyms?

Accepted Answer

Hybrid search combining vector search and full-text BM25 search. Hybrid search in Azure AI Search runs vector (k-NN) search and full-text BM25 keyword search in parallel, then fuses the results using Reciprocal Rank Fusion (RRF). BM25 excels at exact term matching, so domain-specific acronyms that embedding models underrepresent will still be matched lexically, dramatically improving recall for those queries. This is the standard remedy when semantic/vector retrieval alone misses rare tokens.

Answer

Increasing the embedding dimension size to 3072

Answer

Enabling Azure AI Content Safety filters

Answer

Setting the semantic ranker threshold to zero

Question 4

You are implementing an agentic workflow where an agent executes tools that modify corporate data. To adhere to Responsible AI principles, you must ensure a human reviewer approves sensitive tool actions before they are executed. What design pattern should you implement in Azure AI Agent Service?

Accepted Answer

Human-in-the-loop (HITL) approval workflow for tool execution. Human-in-the-loop (HITL) approval inserts a human review step before sensitive tool actions execute, satisfying Responsible AI requirements for oversight of consequential operations. In Azure AI Agent Service, this is implemented by pausing the agent's tool call, surfacing it for approval (via UI, API, or Logic Apps), and only proceeding on explicit human consent. This preserves accountability and prevents autonomous harmful actions.

Answer

Fully autonomous agent loop with max iterations set to 50

Answer

Azure API Management rate limiting policy

Answer

Asynchronous event grid notification after tool execution

Question 5

You are configuring model fine-tuning for an Azure OpenAI model in Azure AI Foundry. You have prepared a JSONL training dataset. Which evaluation approach is recommended to verify that fine-tuning successfully improved model performance without causing catastrophic forgetting?

Accepted Answer

Run an evaluation dataset comparing baseline and fine-tuned models using Azure AI Foundry evaluation metrics. To verify that fine-tuning improved model performance without catastrophic forgetting, you should run an evaluation dataset comparing the baseline (pre-fine-tuning) and fine-tuned models using Azure AI Foundry evaluation metrics. This approach measures improvements on the target task and checks for degradation on general capabilities, providing quantitative evidence of fine-tuning success and detecting catastrophic forgetting.

Answer

Inspect Azure Key Vault access audit logs

Answer

Review Azure Cost Management invoices for training hours

Answer

Check Azure Monitor CPU utilization percentages of the hosting cluster

Question 6

You are designing a RAG solution using Azure AI Search and Azure OpenAI. Which TWO factors significantly influence the effectiveness of chunking documents during the ingestion phase? (Choose TWO)

Accepted Answer

Chunk size measured in tokens or characters. Option A (chunk size measured in tokens or characters) is correct because the size of each chunk directly determines how much context is embedded and retrieved; chunks that are too large dilute semantic relevance and may exceed the embedding model's token limit, while chunks that are too small lack sufficient context, so tuning size in tokens or characters is a primary lever for RAG quality. Option C (chunk overlap percentage between consecutive segments) is correct because overlap preserves continuity across chunk boundaries, preventing sentences or ideas split between segments from losing meaning, and the overlap ratio materially affects retrieval recall and answer coherence. Option B is incorrect because the Azure OpenAI model temperature only controls randomness during text generation at inference time and has no effect on how documents are chunked during ingestion. Option D is incorrect because Azure API Management timeout duration governs request/response time limits for API calls and does not influence chunking strategy. Option E is incorrect because virtual network subnet IP address range is a networking concern for connectivity and has no bearing on document segmentation.

Answer

Azure OpenAI model temperature setting

Answer

Azure API Management timeout duration

Answer

Virtual network subnet IP address range

Question 7

You are configuring an agent in Azure AI Agent Service that needs to utilize tools. Which TWO types of tools are natively supported by Azure AI Agent Service? (Choose TWO)

Accepted Answer

Function calling (Custom tools/APIs). Azure AI Agent Service natively supports Function calling (option C), which lets the agent invoke custom tools and APIs by defining function schemas that the model can call to retrieve data or perform actions. It also natively supports Code Interpreter (option E), a sandboxed Python execution environment used for data analysis, math, and file generation. Options A, B, and D are not native tool types in Azure AI Agent Service: a 'stored procedure compiler,' a 'VM provisioning extension,' and a 'Stream Analytics tumbling window operator' are unrelated Azure capabilities and cannot be attached as agent tools.

Answer

Azure SQL Database stored procedure compiler

Answer

Azure Virtual Machine provisioning extension

Answer

Azure Stream Analytics tumbling window operator

Question 8

You are troubleshooting a RAG application in Azure AI Foundry where retrieved search results are irrelevant to user queries. Which THREE actions should you take to diagnose and improve retrieval quality? (Choose THREE)

Accepted Answer

Evaluate whether document chunk sizes and overlaps align with query granularity. Option A is correct because chunk size and overlap directly control retrieval granularity: if chunks are too large they dilute the embedding with irrelevant content, and if too small they lose context, so aligning them with typical query granularity improves relevance. Option B is correct because inspecting embedding vector distances and similarity scores (e.g., cosine similarity in Azure AI Search) reveals whether the query embedding is actually close to the retrieved documents or whether the embedding model, index, or query vectorization is mismatched. Option E is correct because enabling semantic ranking in Azure AI Search applies a deep language model re-ranker on top of the initial BM25/vector results, promoting semantically relevant documents and typically raising retrieval precision. Option C is not appropriate because temperature controls generative randomness, not retrieval quality, and 1.5 is outside the supported range for most Azure OpenAI models. Option D is not relevant because Key Vault access policies govern secret management, not the relevance of search results.

Answer

Increase the Azure OpenAI model temperature parameter to 1.5

Answer

Configure Azure Key Vault access policies for the search service

Question 9

When designing prompt engineering strategies for complex reasoning tasks in Azure OpenAI, which TWO techniques help reduce hallucinations and improve output reliability? (Choose TWO)

Accepted Answer

Few-shot prompting providing high-quality input-output demonstration examples. Few-shot prompting (A) is correct because supplying several high-quality input-output demonstration examples in the prompt conditions the model on the desired format, tone, and reasoning pattern, which measurably reduces hallucination and improves consistency on complex reasoning tasks. Chain-of-thought prompting (B) is correct because explicitly instructing the model to reason step by step decomposes a hard problem into intermediate steps, improving accuracy on multi-step reasoning and making errors easier to detect. The unmarked options do not belong: removing system prompts (C) strips grounding and behavioral constraints, which increases hallucination risk; setting top_p to 0.0 with temperature 2.0 (D) is a contradictory, degenerate sampling configuration that does not improve reliability; and hex-encoding prompt variables (E) only obscures text and adds no reasoning benefit.

Answer

Disabling system prompts entirely to maximize model freedom

Answer

Setting the top_p parameter to 0.0 while setting temperature to 2.0

Answer

Encoding all prompt variables in hexadecimal format

Question 10

You are evaluating generative AI model outputs in Azure AI Foundry. Which TWO automated evaluation metrics assess the quality and correctness of RAG responses against source data? (Choose TWO)

Accepted Answer

Groundedness (measuring adherence to source context). Option A, Groundedness, is correct because it is a built-in Azure AI Foundry evaluation metric that measures how well a generative AI response is supported by and adheres to the retrieved source context, which is exactly what is needed to verify RAG responses against source data and detect ungrounded or hallucinated content. Option E, Relevance, is correct because it is another built-in Foundry evaluation metric that assesses how well the model's response addresses the user's query, complementing groundedness by judging answer quality and correctness relative to the question. The remaining options are infrastructure and platform telemetry rather than AI quality metrics: B (virtual network gateway throughput) measures network bandwidth, C (storage account transaction count per hour) measures storage operations, and D (CPU utilization percentage of the Azure App Service plan) measures compute load, none of which evaluate the semantic quality or correctness of RAG responses.

Answer

Virtual network gateway throughput

Answer

Storage account transaction count per hour

Answer

CPU utilization percentage of the Azure App Service plan

Question 11

You are deploying an enterprise RAG application in Azure AI Foundry. You need to ensure data security and compliance across all components. Which THREE security practices should you implement? (Choose THREE)

Accepted Answer

Use Managed Identities with Azure RBAC for service-to-service authentication. Option A is correct because Managed Identities combined with Azure RBAC eliminate the need to embed credentials in code or configuration, allowing the RAG application's components (e.g., Azure OpenAI, Azure AI Search, Storage) to authenticate to each other through Microsoft Entra ID tokens with least-privilege role assignments. Option C is correct because Azure Private Endpoints place Azure OpenAI and Azure AI Search on a private IP within your virtual network, removing exposure to the public internet and satisfying network isolation and data-exfiltration compliance requirements. Option E is correct because Customer-Managed Keys stored in Azure Key Vault give you control over the encryption keys protecting data at rest in Azure OpenAI, Azure AI Search, and storage accounts, which is a common regulatory requirement for key ownership and revocation. Option B is not correct because granting anonymous public network access on AI service firewalls exposes the services to unauthenticated internet traffic and violates the security and compliance goals of the scenario. Option D is not correct because storing API keys in plain text in public GitHub repositories leaks credentials and is a severe security anti-pattern that contradicts every stated requirement.

Answer

Grant anonymous public network access on all AI service firewalls

Answer

Store API keys in plain text configuration files committed to public GitHub repositories

Question 12

You are optimizing an Azure OpenAI deployment in Azure AI Foundry for high-throughput batch processing. Which TWO strategies or configurations should you consider? (Choose TWO)

Accepted Answer

Azure OpenAI Batch API for asynchronous high-volume jobs at reduced costs. Option A is correct because the Azure OpenAI Batch API is purpose-built for asynchronous, high-volume workloads, processing requests in the background with a separate quota and offering roughly 50% lower cost than synchronous calls, which directly suits high-throughput batch processing. Option C is correct because Provisioned Throughput Units (PTUs) reserve dedicated model capacity for a deployment, guaranteeing consistent throughput and predictable latency rather than being throttled by shared pay-as-you-go quota, which is essential for sustained high-volume jobs. Option B is wrong because disabling SSL certificate validation weakens transport security and is never a valid optimization strategy. Option D is wrong because setting max_tokens to 1 would truncate responses to a single token, breaking output quality rather than improving throughput. Option E is wrong because routing production traffic through public developer workstations is insecure, unreliable, and not a supported architecture for scaling Azure OpenAI workloads.

Answer

Disabling SSL certificate validation in the client SDK

Answer

Setting model max_tokens to 1

Answer

Routing all traffic through public developer local workstations

Question 13

When designing agentic workflows in Azure AI Foundry, what are THREE key characteristics or components of agentic design compared to traditional single-turn prompt completion? (Choose THREE)

Accepted Answer

Tool use and external API invocation capabilities. Option A (Tool use and external API invocation capabilities) is correct because agentic workflows in Azure AI Foundry extend beyond text generation by letting agents call tools, functions, and external APIs (for example via function calling or Azure AI Foundry tool connections) to retrieve data or perform actions. Option C (Iterative reasoning and self-reflection loops) is correct because agents can evaluate intermediate results, critique their own output, and re-plan across multiple reasoning steps rather than producing one final completion. Option D (Autonomous planning and task decomposition) is correct because agentic design centers on breaking a high-level goal into subtasks and deciding the order of execution dynamically, often orchestrated by the model itself. Option B is incorrect because hardcoding every conditional path is the opposite of agentic design, which relies on model-driven decisions and orchestration rather than fully procedural branching. Option E is incorrect because single stateless prompt completions without memory describe traditional single-turn completion, whereas agentic workflows typically maintain state, context, and memory across turns.

Answer

Hardcoding every conditional execution path in procedural code

Answer

Executing single stateless prompt completions without memory

Question 14

You are designing a secure RAG retrieval pipeline in Azure AI Search. Which THREE indexing or search configurations are essential when handling confidential documents? (Choose THREE)

Accepted Answer

Encryption at rest using Customer-Managed Keys (CMK). Option A is correct because Azure AI Search supports encryption at rest with Customer-Managed Keys (CMK) via Azure Key Vault, which lets you control the key lifecycle and revoke access to confidential index data, a requirement for sensitive workloads. Option B is correct because Private Endpoint connections (via Azure Private Link) give the search service a private IP inside your VNet, so indexing and query traffic never traverses the public internet, isolating confidential retrieval traffic. Option D is correct because document-level security trimming with OData filters (e.g., filtering on a groups or ACL field using search.in or eq) ensures each user only retrieves documents they are authorized to see, preventing leakage of confidential content in RAG responses. Option C is wrong because disabling authentication would expose the index to anonymous guest queries, directly violating confidentiality. Option E is wrong because storing unencrypted indexes in public blob containers removes both encryption and network isolation, which is unacceptable for confidential documents.

Answer

Disabling all authentication to allow open guest queries

Answer

Storing unencrypted search indexes in public Azure blob containers

Question 15

When implementing prompt engineering for generative AI applications in Azure AI Foundry, which TWO practices are considered best practices for maintaining prompt maintainability and security? (Choose TWO)

Accepted Answer

Storing and versioning prompt templates as assets in Azure AI Foundry. Option C is correct because Azure AI Foundry supports storing and versioning prompt templates as managed assets, which enables change tracking, rollback, reuse across deployments, and consistent governance instead of scattering prompts through application code. Option E is correct because separating system instructions from untrusted user input with clear delimiters or XML tags helps prevent prompt injection and instruction-override attacks, and it makes the prompt structure easier to read and maintain. Options A, B, and D are not best practices: unformatted single-paragraph prompts are hard to review and maintain, embedding API keys in prompt strings exposes secrets that can leak through logs or model output, and hardcoding user names and passwords into system prompts creates a serious credential disclosure risk and violates the principle of keeping secrets out of prompts.

Answer

Writing all prompts as single unending paragraphs without formatting

Answer

Embedding API keys directly inside prompt strings

Answer

Hardcoding user names and passwords into system prompts