AI-103 Practice Test 5 — 10 Questions

Question 1

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?

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

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.

Answer

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 2

You are implementing a disaster recovery strategy for a critical image processing application that relies on Azure AI Vision. If the primary Azure region experiences an outage, how should you configure failover?

Accepted Answer

Deploy a secondary Azure AI Vision resource in a paired Azure region and configure your application to fail over to the secondary endpoint if errors occur.. Deploying a secondary Azure AI Vision resource in a paired region and updating the application configuration (or using Azure Front Door / Traffic Manager) enables regional failover.

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Use geo-replication on the Azure Blob Storage account to automatically fail over the AI service endpoints.

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Configure Azure Site Recovery to replicate the Azure AI Vision service backend VMs.

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Rely on automatic synchronous replication of Custom Vision models across all global Azure datacenters without application changes.

Question 3

You are integrating Azure OpenAI into an enterprise application. You need to prevent prompt injection attacks where users attempt to override the system instructions. Which approach provides the most robust defense?

Accepted Answer

Implement an input guardrail using Azure AI Content Safety to detect and block jailbreak attempts before they reach the model.. Using dual-prompt architectures or Azure AI Content Safety groundedness checks provides robust detection and mitigation against prompt injections.

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Set the model frequency penalty to 2.0 to penalize repeated malicious phrases.

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Append 'Ignore previous instructions' at the end of every user message.

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Encode all user input in Base64 before sending it to the model.

Question 4

You are implementing indirect prompt injection defenses in a RAG application where documents are ingested from public web pages. Which THREE mitigation strategies should you deploy? (Choose THREE)

Accepted Answer

Use strict structural delimiters (like XML tags) to isolate retrieved context from system instructions. Prompt shields in Content Safety, strict prompt delimiters separating data from instructions, and content validation filters protect against indirect prompt injection.

Answer

Trust all incoming web page text unconditionally without inspection

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Disable all content moderation filters to maximize processing speed

Question 5

You are orchestrating a multi-agent system in Microsoft Foundry where Agent A generates code and Agent B reviews it. Agent B frequently gets stuck in a loop trying to optimize minor syntax formatting. How should you design the agentic workflow to prevent this infinite refinement loop?

Accepted Answer

Configure a maximum turn counter and state transition condition in the orchestrator workflow to terminate the loop after a set threshold.. Establishing a strict iteration limit or state machine check within the orchestrator prevents agents from looping indefinitely.

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Disable function calling for Agent B so it can only output text responses.

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Increase the model's presence penalty so Agent B is forced to generate novel review comments.

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Use a smaller model for Agent B to reduce the quality of its reviews.

Question 6

You are developing a customer support agent using Microsoft Foundry. The agent needs to call a custom weather API tool when a user asks about local conditions. How should you define this capability in the agent configuration?

Accepted Answer

Register a custom function tool by providing an OpenAPI schema definition that describes the weather API endpoints, parameters, and expected responses.. In Microsoft Foundry and Azure AI Agent Service, you configure custom tools by defining them as OpenAPI specifications or functions that the model can invoke via function calling capabilities.

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Upload the compiled C# library containing the API client code directly into the agent code interpreter workspace.

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Add the weather API endpoint URL as a grounding data source under the vector store settings.

Answer

Configure a managed data connection pointing to the weather API URL within Azure AI Search.

Question 7

You are designing an information extraction solution using Azure AI Document Intelligence. Which TWO features are supported by Custom Neural models? Each correct selection represents a complete solution element.

Accepted Answer

Extraction of key-value pairs and fields defined during training. Custom Neural models support extracting both individual key-value fields and complex tables from unstructured or semi-structured documents.

Answer

Zero-shot extraction of completely unseen document types without any training data

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Real-time voice-to-text transcription of document contents

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Automatic language translation of extracted text into 100+ languages

Question 8

You need to extract data from a document that includes handwritten signatures and printed text. Which capability is required?

Accepted Answer

Handwritten OCR (Read). The Read capability, which is part of the Document Intelligence service, supports both machine-printed text and handwriting.

Answer

Object Detection

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Custom Template model

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Form Recognizer 2.0 API

Question 9

You are optimizing an agentic workflow in Azure AI Foundry. The agent needs to solve complex reasoning problems by breaking them down into distinct sub-tasks, executing them iteratively, and reflecting on intermediate results. Which THREE architectural frameworks or patterns support this behavior? (Choose three.)

Accepted Answer

Reflection pattern where the agent critiques its own intermediate outputs and refines them.. Chain-of-thought, ReAct (Reason+Act), and plan-and-solve are foundational agentic reasoning patterns.

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Static hardcoded IF-ELSE decision trees without model inference.

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Single-turn stateless completion calls with temperature set to zero.

Question 10

As part of responsible AI practices in Azure AI Foundry, which TWO actions should you perform to evaluate and mitigate model fairness and safety risks before deployment? (Choose two)

Accepted Answer

Run automated evaluations in Azure AI Foundry to measure metrics such as groundedness, relevance, and protected material presence.. Evaluating models using automated evaluation metrics (such as groundedness and relevance) and conducting red teaming are essential responsible AI practices.

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Bypass security reviews if the application is intended for internal enterprise employees only.

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Remove all system prompts to allow the model complete autonomy during evaluation runs.

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Hardcode responses for all possible user inputs to eliminate model non-determinism entirely.