AI-103 Practice Test 8 — 10 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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

Answer

Azure Stream Analytics job cluster

Answer

Power BI embedded analytics workspace

Question 10

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