AI-103 Practice Test 2 — 30 Questions

Question 1

You are optimizing an application that calls Azure AI Vision Read API. Which THREE techniques help reduce latency and improve overall throughput? (Choose three.)

Accepted Answer

Resize extremely large images to optimal dimensions while maintaining readability.. Throughput and latency improvements include resizing excessively large images before upload, using asynchronous polling efficiently without tight busy-wait loops, and co-locating resources.

Answer

Send uncompressed RAW bitmap data to bypass image decoding overhead.

Answer

Disable SSL/TLS verification to speed up TCP handshakes.

Question 2

Your enterprise application uses Azure AI Vision. Security policies require that all API keys are rotated every 30 days without causing application downtime. How should you manage the key rotation?

Accepted Answer

Switch client applications to Key 2, regenerate Key 1 in the Azure portal, switch clients to Key 1, and then regenerate Key 2.. Azure AI Services resources provide two keys (Key 1 and Key 2). To rotate keys without downtime, update applications to use Key 2, regenerate Key 1, update applications to use Key 1, and then regenerate Key 2.

Answer

Restart the Azure App Service hosting the client application.

Answer

Delete the Azure AI service resource and create a new one every 30 days.

Answer

Disable authentication on the resource during the rotation window.

Question 3

A user asks a question that triggers a 'Blocked' response from Azure AI Content Safety, even though the query seems harmless. What is the best step to investigate this?

Accepted Answer

Check the Content Safety logs in the Azure AI Foundry portal.. Viewing the logs in the Content Safety dashboard identifies which specific policy was triggered.

Answer

Change the system prompt to ignore safety.

Answer

Increase the model temperature.

Answer

Disable Content Safety for the deployment.

Question 4

You are developing a .NET application that interacts with Azure AI Vision Read API. Which TWO objects or properties would you inspect in the asynchronous response to retrieve extracted text lines and their confidence scores? (Choose two.)

Accepted Answer

text. The Read API response contains `analyzeResult.readResults` or `readResult.blocks`/`lines`, where each line has `text`, `boundingBox`, and `confidence` (in older versions) or text content.

Answer

dominantColors

Answer

adultScore

Answer

smartCrops

Question 5

You want to extract gender and age attributes from human faces in photos. While Azure AI Face service handles facial analysis, which base Azure AI Vision 4.0 feature detects the presence and bounding boxes of people?

Accepted Answer

features=people. The 'people' feature in Image Analysis 4.0 detects humans and returns their bounding boxes.

Answer

features=objects

Answer

features=tags

Answer

features=read

Question 6

You are implementing a multi-turn conversational agent in Azure AI Foundry. As the conversation grows longer, API latency and costs increase significantly. Which strategy should you implement to manage the conversation history effectively?

Accepted Answer

Implement a sliding window history buffer combined with periodic summarization of older dialogue turns.. Summarizing older conversation turns or maintaining a sliding window of recent messages keeps token counts manageable.

Answer

Increase max_tokens to accommodate infinite history growth.

Answer

Disable the chat history array entirely and treat every turn as a standalone prompt.

Answer

Truncate the entire conversation history after every single user turn.

Question 7

Which TWO image file formats are natively supported by Azure AI Vision for image analysis? (Choose two.)

Accepted Answer

JPEG. Azure AI Vision supports standard web formats including JPEG, PNG, GIF, BMP, WEBP, and TIFF (for Read).

Answer

MP3

Answer

MKV

Answer

WAV

Question 8

You are testing a Custom Vision model via the REST API. You receive an HTTP 400 Bad Request error with the message 'InvalidProjectId'. What is the most likely cause?

Accepted Answer

The project ID GUID in the request URL is incorrect or does not exist in this Custom Vision account.. The project ID GUID provided in the URL path is malformed, does not exist, or belongs to a different region/resource.

Answer

The image file format is invalid.

Answer

The subscription has reached its monthly quota limit.

Answer

The prediction key header was omitted.

Question 9

You are designing an agentic workflow in Azure AI Agent Service where multiple specialized agents need to collaborate to solve complex, multi-step financial analysis tasks. One agent manages the workflow state and decides which sub-agent to invoke next. What architectural pattern is best suited for this implementation?

Accepted Answer

Orchestrator-workers pattern, where a central agent dynamically assigns sub-tasks to specialized worker agents based on intermediate results.. The orchestrator-workers (or router) pattern uses a central orchestrator agent to manage state, evaluate progress, and delegate tasks to specialized worker agents.

Answer

Single monolithic prompt pattern, where all instructions are combined into one massive system prompt without multi-agent handoffs.

Answer

Map-reduce batch processing pattern, where all sub-agents execute independently in parallel without communication.

Answer

Static sequential pipeline, where agents execute strictly in a predefined linear order without conditional branching.

Question 10

Your team is implementing 'Human-in-the-loop' (HITL) for an agent that performs account updates. Which mechanism should you use to pause the agent and wait for approval?

Accepted Answer

Use a Prompt Flow 'Pause' node or custom state management logic.. In agentic workflows, an interruption or 'callback' mechanism allows the system to pause execution until human input is received.

Answer

Set a long timeout in the API call.

Answer

Increase the model's 'stop_sequences'.

Answer

Use an infinite loop in Python.

Question 11

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. 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

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 12

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.

Answer

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

Answer

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

Answer

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

Question 13

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.

Answer

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

Answer

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

Answer

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

Question 14

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

Answer

Disable all content moderation filters to maximize processing speed

Question 15

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.

Answer

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

Answer

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

Answer

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

Question 16

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.

Answer

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

Answer

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 17

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

Answer

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

Answer

Automatic language translation of extracted text into 100+ languages

Question 18

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

Answer

Custom Template model

Answer

Form Recognizer 2.0 API

Question 19

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.

Answer

Static hardcoded IF-ELSE decision trees without model inference.

Answer

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

Question 20

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.

Answer

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

Answer

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

Answer

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