AI-103 Multiple Select — 50 Questions

Question 1

Which TWO metrics are reported by Document Intelligence Studio to evaluate model performance?

Accepted Answer

Recall. Precision and Recall are the core metrics provided for every field in a custom model.

Answer

Throughput

Answer

Latencey

Answer

Confidence score

Question 2

You are designing an image analysis solution using Azure AI Vision 4.0. Which TWO visual features can be requested in a single Image Analysis API analyze request? (Choose two.)

Accepted Answer

tags. Image Analysis 4.0 supports combining multiple features such as 'tags', 'objects', 'read', 'caption', 'denseCaptions', 'smartCrops', 'people', and 'brands' in one analyze call.

Answer

Custom Vision Classification

Answer

Azure AI Face Analyze

Answer

Azure AI Document Intelligence Layout

Question 3

Which TWO factors directly influence the embedding quality and retrieval relevance in an Azure AI Search vector retrieval pipeline? (Choose two.)

Accepted Answer

The choice of embedding model (e.g., text-embedding-3-large vs text-embedding-3-small) and its dimensionality.. Embedding model choice and chunking strategy are primary determinants of vector retrieval quality.

Answer

The background color of the source documents in blob storage.

Answer

The font family used in the original PDF documents.

Answer

The client browser version used by the end user.

Question 4

Your enterprise requires strict security and governance over your Azure AI Vision resources. Which THREE security configurations should you implement? (Choose three.)

Accepted Answer

Configure Azure Private Endpoints to restrict access to your Virtual Network.. Enterprise security for Azure AI Services includes Private Endpoints, disabling local authentication in favor of Microsoft Entra ID (RBAC), and Key Vault integration for secrets.

Answer

Store API keys in plain text configuration files on public GitHub repositories.

Answer

Disable TLS encryption to improve API response latency.

Question 5

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 6

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 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 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 9

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 10

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 11

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.

Question 12

You are configuring prompt engineering practices for a generative AI application in Azure AI Foundry. Which THREE practices should you follow to ensure high output quality and reliability? (Choose three.)

Accepted Answer

Specify the desired output format explicitly (e.g., JSON schema or markdown tables).. Effective prompt engineering involves clear separation of context/instructions, few-shot examples, and specifying output formats.

Answer

Embed all system instructions inside the user prompt string without using system roles.

Answer

Rely entirely on vague, implicit instructions to give the model creative freedom.

Question 13

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 with clear examples and chain-of-thought reasoning significantly improve complex task accuracy and reduce hallucinations.

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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