AI-103 Practice Test 3 — 30 Questions

Question 1

You are designing a generative AI application in Azure AI Foundry and want to categorize user queries into predefined intent classes before routing them to specific agents. Which prompt engineering technique should you apply?

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

Accepted Answer

Few-shot prompting with labeled classification examples. Few-shot classification prompting provides examples of inputs paired with their corresponding categories to guide the model's classification accuracy.

Answer

Zero-shot random generation

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Enabling JSON output mode without schema definition

Answer

Increasing model temperature to 1.5

Question 2

You are building a generative AI application that summarizes long legal documents. Users complain that summaries omit important clauses located in the middle of the document due to the model's attention limitations over extremely long texts. What phenomenon does this represent, and how can you mitigate it?

Accepted Answer

Lost in the middle phenomenon; mitigate using map-reduce or recursive summarization patterns. The 'lost in the middle' phenomenon occurs when LLMs overlook information in the middle of long contexts. Mitigation involves chunking or map-reduce summarization patterns.

Answer

Token overflow error; mitigate by increasing Azure AI Search semantic ranker depth

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Embedding drift; mitigate by retraining the text-embedding-ada-002 model

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Prompt injection; mitigate by enabling Azure AI Content Safety prompt shields

Question 3

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) review pauses agent execution before tool calls that perform write or destructive actions, awaiting explicit human approval.

Answer

Fully autonomous agent loop with max iterations set to 50

Answer

Azure API Management rate limiting policy

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Asynchronous event grid notification after tool execution

Question 4

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. Batch API usage and provisioned throughput units (PTUs) are designed for managing high-volume enterprise throughput and batch 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 5

When configuring vector embeddings and search indexes in Azure AI Search for RAG, which THREE factors directly impact vector search performance and recall accuracy? (Choose THREE)

Accepted Answer

Embedding model dimension size and consistency with source generation. Embedding model dimensionality, vector similarity metric (e.g., cosine, dot product), and HNSW algorithm index parameters impact search performance and recall.

Answer

Azure App Service CPU architecture (x64 vs ARM)

Answer

Configuring Azure Front Door DNS CNAME records

Question 6

You are preparing a prompt template for a summarization task. You want to provide three distinct examples of input text and expected summaries before asking the model to process the target text. What is this prompting technique called?

Accepted Answer

Few-shot prompting. Providing examples within the prompt is known as few-shot prompting. Zero-shot has no examples, and chain-of-thought involves reasoning steps.

Answer

Retrieval-augmented generation

Answer

Chain-of-thought prompting

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Zero-shot prompting

Question 7

You are monitoring your Azure OpenAI deployment in Azure AI Foundry. You notice an increase in HTTP 429 status codes. What does this error indicate?

Accepted Answer

Rate limit or token quota exceeded (Too Many Requests). HTTP 429 indicates that the request rate or token usage has exceeded your allocated quota or rate limit.

Answer

Internal server error at Microsoft

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Model deployment not found

Answer

Invalid API key authentication

Question 8

You are auditing a generative AI application for responsible AI compliance. Which THREE practices should you implement to mitigate risks? (Choose 3)

Accepted Answer

Configure system messages to explicitly define boundaries. Responsible AI involves monitoring, filtering, and transparency, including content filtering and human-in-the-loop validation.

Answer

Store all user PII in plain text logs

Answer

Increase the temperature of the model to 1.0

Question 9

You are managing prompt assets in Azure AI Foundry. Your team collaborates on developing, testing, and versioning system prompts. Which feature in Azure AI Foundry should you use to store and manage these prompt templates?

Accepted Answer

Prompt flow / Prompt management workspace assets. Prompt flow and prompt management tools in Azure AI Foundry allow teams to author, version, and share prompt templates collaboratively.

Answer

Azure Storage Blob lifecycle management policies

Answer

Azure App Service deployment slots

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Azure Key Vault secret versions

Question 10

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. Planning, tool use, and multi-step reasoning loops distinguish agentic workflows from traditional single-turn API calls.

Answer

Hardcoding every conditional execution path in procedural code

Answer

Executing single stateless prompt completions without memory

Question 11

You are monitoring an Azure OpenAI model deployed in Azure AI Foundry. Which TWO Azure diagnostic metrics or logs should you analyze to track operational health and performance? (Choose TWO)

Accepted Answer

Request duration (latency) in milliseconds. Request duration (latency) and token counts (prompt and completion tokens) are key operational metrics tracked in diagnostic logs.

Answer

Azure ExpressRoute circuit bandwidth saturation

Answer

Azure SQL Database deadlock frequency

Answer

Virtual machine disk IOPS consumption

Question 12

You are designing a customer service chatbot using Azure OpenAI. You need to ensure that the model refuses to answer questions outside the scope of your company's products. Where should you define this boundary instruction?

Accepted Answer

In the system message. System messages (or system prompts) set the behavior, tone, and operational boundaries of the model throughout the conversation.

Answer

In the temperature setting

Answer

In the user prompt prefix

Answer

In the max_tokens parameter configuration

Question 13

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. Analyzing embeddings, evaluating chunking strategies, and testing hybrid search or semantic ranking are key steps to diagnose and resolve poor retrieval quality.

Answer

Increase the Azure OpenAI model temperature parameter to 1.5

Answer

Configure Azure Key Vault access policies for the search service

Question 14

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 combines vector search (for semantic meaning) with full-text keyword search (BM25 algorithm), ensuring exact acronyms and part numbers are matched reliably.

Answer

Increasing the embedding dimension size to 3072

Answer

Enabling Azure AI Content Safety filters

Answer

Setting the semantic ranker threshold to zero

Question 15

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. Using private endpoints, managed identities, and customer-managed keys are foundational enterprise security practices for Azure AI and search services.

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 16

You are troubleshooting a multi-agent workflow in Azure AI Foundry where an agent fails to invoke an external API tool correctly. Which THREE debugging steps should you perform? (Choose THREE)

Accepted Answer

Test network connectivity and authentication credentials between the agent service and the external API endpoint. Inspecting tool definition JSON schemas, reviewing model output tool call arguments, and verifying API endpoint connectivity are key troubleshooting steps.

Answer

Restart the client web browser session

Answer

Increase the Azure DNS TTL value for domain resolution

Question 17

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). Groundedness and relevance are core Azure AI Foundry automated evaluation metrics used to assess RAG application quality.

Answer

Virtual network gateway throughput

Answer

Storage account transaction count per hour

Answer

CPU utilization percentage of the Azure App Service plan

Question 18

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). Security trimming (filters), encrypted indexes, and restricted network access via private endpoints are essential for confidential RAG pipelines.

Answer

Disabling all authentication to allow open guest queries

Answer

Storing unencrypted search indexes in public Azure blob containers

Question 19

When designing an Azure AI Foundry project architecture, which TWO resources are typically linked to the Azure AI Hub to enable full model deployment and search capabilities? (Choose TWO)

Accepted Answer

Azure AI Search service. Azure OpenAI and Azure AI Search are core resources linked to Azure AI Hub workspaces for model hosting and RAG search indexer capabilities.

Answer

Azure SQL Database relational server

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Azure Stream Analytics job cluster

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Power BI embedded analytics workspace

Question 20

When designing a Responsible AI evaluation workflow in Azure AI Foundry, which TWO metrics are specifically used to assess content safety and potential harms? (Choose two.)

Accepted Answer

Hate speech and unfairness detection. Violence, sexual, hate, and self-harm detection along with protected material detection are core safety metrics in Azure AI Foundry.

Answer

Word count frequency

Answer

BLEU machine translation score

Answer

Model parameter count