Sample questions
Microsoft Azure AI Engineer Associate AI-102 practice questions
Drag and drop the steps to set up Azure AI Content Safety for content moderation into the correct order.
Drag steps to the numbered slots on the right, or tap a step then tap a slot.
Drag and drop the steps to configure an Azure AI Search index with a custom skill into the correct order.
Drag steps to the numbered slots on the right, or tap a step then tap a slot.
Drag and drop the steps to deploy a custom language model using Azure AI Language into the correct order.
Drag steps to the numbered slots on the right, or tap a step then tap a slot.
Drag and drop the steps to implement an Azure AI Bot Service with QnA Maker into the correct order.
Drag steps to the numbered slots on the right, or tap a step then tap a slot.
A company is using Azure AI Vision to analyze images from a manufacturing line. The solution must detect defects in real-time. The team discovers that the model's accuracy drops significantly when images are captured under different lighting conditions. What is the best approach to improve the model's robustness?
Trap 1: Apply image pre-processing to normalize lighting before sending to…
Pre-processing helps but does not address the root cause if the model was not trained on varied data.
Trap 2: Increase the number of training images without varying lighting…
More images without variation will not help the model generalize to different lighting.
Trap 3: Use a pre-built model from Azure AI Vision instead of a custom…
Pre-built models are not specialized for defect detection in manufacturing.
- A
Apply image pre-processing to normalize lighting before sending to the model.
Why wrong: Pre-processing helps but does not address the root cause if the model was not trained on varied data.
- B
Increase the number of training images without varying lighting conditions.
Why wrong: More images without variation will not help the model generalize to different lighting.
- C
Retrain the model using images captured under various lighting conditions, using data augmentation.
Including diverse lighting in training data and using augmentation improves robustness.
- D
Use a pre-built model from Azure AI Vision instead of a custom model.
Why wrong: Pre-built models are not specialized for defect detection in manufacturing.
A company is deploying a generative AI solution using Azure OpenAI Service to generate product descriptions. The solution must comply with responsible AI principles, specifically ensuring that generated content does not include harmful or offensive language. Which Azure AI service feature should they implement to automatically filter the output?
Trap 1: Configure Prompt Shields in Azure AI Content Safety.
Prompt Shields protect against prompt injection attacks, not automatically filter harmful content.
Trap 2: Use Azure AI Content Safety with a custom category severity…
Custom Content Safety is for custom categories, but the question asks for automatic filtering of harmful content, which is built into the default content filtering.
Trap 3: Use groundedness detection in Azure AI Content Safety.
Groundedness detection checks if the model's output is based on source material, not for harmful content.
- A
Enable the default content filtering system in Azure OpenAI Service.
Azure OpenAI Service includes a default content filtering system that automatically filters harmful content based on severity levels.
- B
Configure Prompt Shields in Azure AI Content Safety.
Why wrong: Prompt Shields protect against prompt injection attacks, not automatically filter harmful content.
- C
Use Azure AI Content Safety with a custom category severity threshold.
Why wrong: Custom Content Safety is for custom categories, but the question asks for automatic filtering of harmful content, which is built into the default content filtering.
- D
Use groundedness detection in Azure AI Content Safety.
Why wrong: Groundedness detection checks if the model's output is based on source material, not for harmful content.
Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?
Trap 1: Azure AI Speech and Azure OpenAI
Speech is for voice; lacks document processing.
Trap 2: Azure AI Language and Azure AI Document Intelligence
No search engine for retrieval.
Trap 3: Azure AI Document Intelligence and Azure Bot Service
Document Intelligence extracts data but does not provide search index for Q&A.
- A
Azure AI Search and Azure OpenAI
Search indexes the manuals; Azure OpenAI provides conversational Q&A (RAG pattern).
- B
Azure AI Speech and Azure OpenAI
Why wrong: Speech is for voice; lacks document processing.
- C
Azure AI Language and Azure AI Document Intelligence
Why wrong: No search engine for retrieval.
- D
Azure AI Document Intelligence and Azure Bot Service
Why wrong: Document Intelligence extracts data but does not provide search index for Q&A.
A company is building a custom question-answering solution using Azure AI Language. They need to ensure that the model can provide answers from a set of internal documents, but only to authenticated users from the company's Azure Active Directory tenant. The solution should minimize latency and cost. Which deployment option should the team choose?
Trap 1: Use the public endpoint of Azure AI Language with Azure AD…
Public endpoint does not inherently restrict access to the company's tenant; it requires additional network controls.
Trap 2: Deploy a serverless endpoint with Azure Functions and Azure…
Serverless may have cold-start latency and is not ideal for frequent queries; also introduces more complexity.
Trap 3: Use the prebuilt question-answering model from Azure AI Language…
Prebuilt models cannot be customized with internal documents; they only answer from a predefined FAQ.
- A
Deploy a custom question-answering model using a dedicated Azure AI Language resource with a private endpoint and managed identity.
This provides secure access via private endpoint and managed identity, and dedicated resources ensure low latency and predictable cost.
- B
Use the public endpoint of Azure AI Language with Azure AD token-based authentication.
Why wrong: Public endpoint does not inherently restrict access to the company's tenant; it requires additional network controls.
- C
Deploy a serverless endpoint with Azure Functions and Azure Cognitive Search.
Why wrong: Serverless may have cold-start latency and is not ideal for frequent queries; also introduces more complexity.
- D
Use the prebuilt question-answering model from Azure AI Language with a custom answer list.
Why wrong: Prebuilt models cannot be customized with internal documents; they only answer from a predefined FAQ.
A developer is configuring an Azure AI Language resource for sentiment analysis. The solution must process social media posts in real-time with a throughput of 1000 requests per minute. After testing, the developer notices that the API returns a 429 (Too Many Requests) error when the load exceeds 500 requests per minute. What is the most likely cause and solution?
Trap 1: Scale out the resource by creating multiple Azure AI Language…
Rate limits are per resource; multiple instances would increase total throughput but not the limit per instance.
Trap 2: Implement retry logic with exponential backoff to handle 429 errors.
This is a workaround but does not address the root cause of insufficient capacity.
Trap 3: Use Azure API Management to cache responses and reduce calls.
Caching may not help with real-time processing and does not resolve the rate limit issue.
- A
Scale out the resource by creating multiple Azure AI Language instances and load balancing requests.
Why wrong: Rate limits are per resource; multiple instances would increase total throughput but not the limit per instance.
- B
Upgrade the Azure AI Language resource to a higher tier (e.g., Standard S) to increase the rate limit.
The Free tier has a limit of 20 requests per minute; upgrading to Standard S allows up to 1000 requests per minute.
- C
Implement retry logic with exponential backoff to handle 429 errors.
Why wrong: This is a workaround but does not address the root cause of insufficient capacity.
- D
Use Azure API Management to cache responses and reduce calls.
Why wrong: Caching may not help with real-time processing and does not resolve the rate limit issue.
A developer is tasked with integrating Azure OpenAI Service into an application that generates product descriptions. The developer needs to ensure that the generated content does not contain offensive language. Which Azure AI service should be used in addition to Azure OpenAI?
Trap 1: Azure AI Search
Azure AI Search is for indexing and search, not content moderation.
Trap 2: Azure AI Vision
Azure AI Vision analyzes images, not text.
Trap 3: Azure AI Language
Azure AI Language provides sentiment analysis and entity recognition, not content safety.
- A
Azure AI Search
Why wrong: Azure AI Search is for indexing and search, not content moderation.
- B
Azure AI Vision
Why wrong: Azure AI Vision analyzes images, not text.
- C
Azure AI Language
Why wrong: Azure AI Language provides sentiment analysis and entity recognition, not content safety.
- D
Azure AI Content Safety
Azure AI Content Safety detects offensive language and other harmful content.
A developer is using Azure OpenAI to generate code snippets. The developer needs to ensure that the generated code does not contain security vulnerabilities. Which TWO actions should the developer take? (Choose two.)
Trap 1: Set the max_tokens parameter to a high value to allow longer…
Increasing max_tokens does not affect the security of the generated code.
Trap 2: Fine-tune the model on a dataset containing examples of insecure…
Fine-tuning on insecure code would teach the model to produce more vulnerabilities.
Trap 3: Add a system message that instructs the model to never generate…
System messages can provide guidance but do not guarantee compliance, and the model may still generate insecure code.
- A
Include examples of secure coding practices in the prompt.
Providing examples of secure code helps guide the model towards generating secure code.
- B
Set the max_tokens parameter to a high value to allow longer outputs.
Why wrong: Increasing max_tokens does not affect the security of the generated code.
- C
Fine-tune the model on a dataset containing examples of insecure code.
Why wrong: Fine-tuning on insecure code would teach the model to produce more vulnerabilities.
- D
Use the content filtering feature to block malicious code patterns.
Content filtering can be configured to detect and block common insecure code patterns.
- E
Add a system message that instructs the model to never generate insecure code.
Why wrong: System messages can provide guidance but do not guarantee compliance, and the model may still generate insecure code.
A company uses Azure Form Recognizer to extract data from invoices. The extracted data contains many errors for a specific vendor's invoices. What should they do?
Trap 1: Use a different prebuilt model.
Other prebuilt models may not cover invoices.
Trap 2: Disable the OCR step.
OCR is essential for text extraction.
Trap 3: Increase the confidence threshold.
May reduce errors but not address format.
- A
Use a different prebuilt model.
Why wrong: Other prebuilt models may not cover invoices.
- B
Disable the OCR step.
Why wrong: OCR is essential for text extraction.
- C
Increase the confidence threshold.
Why wrong: May reduce errors but not address format.
- D
Custom train a model with labeled examples of that vendor's invoices.
Custom model learns vendor-specific layouts.
A news organization uses Azure Video Indexer to generate transcripts of live broadcasts. They notice that the speaker names are not appearing in the transcript. What is the most likely cause?
Trap 1: The video resolution is too low for OCR.
OCR extracts text, not speaker names.
Trap 2: The video format is not supported.
Format support does not affect speaker identification.
Trap 3: The language is not set correctly.
Language detection affects transcription, not speaker names.
- A
The video resolution is too low for OCR.
Why wrong: OCR extracts text, not speaker names.
- B
The speaker identification model has not been trained with voice samples.
Speaker names require custom voice identification.
- C
The video format is not supported.
Why wrong: Format support does not affect speaker identification.
- D
The language is not set correctly.
Why wrong: Language detection affects transcription, not speaker names.
A retail company uses Azure Computer Vision to analyze in-store camera feeds. They recently added a new product line and updated the object detection model. However, the model fails to detect the new products. What should the company do first?
Trap 1: Use the pre-built 'products' model from Computer Vision.
Pre-built models may not include the new product line.
Trap 2: Increase the confidence threshold in the API call.
Adjusting threshold does not add new objects to detect.
Trap 3: Recreate the Computer Vision resource in a different region.
Region change does not affect model capabilities.
- A
Use the pre-built 'products' model from Computer Vision.
Why wrong: Pre-built models may not include the new product line.
- B
Increase the confidence threshold in the API call.
Why wrong: Adjusting threshold does not add new objects to detect.
- C
Retrain the custom object detection model with images of the new products.
Custom models need retraining with new labeled data.
- D
Recreate the Computer Vision resource in a different region.
Why wrong: Region change does not affect model capabilities.
Which TWO Azure services can be used to detect and redact PII from images containing text? (Choose two.)
Trap 1: Azure Form Recognizer.
Extracts data from forms, not general PII redaction.
Trap 2: Azure Video Analyzer for Media.
Primarily for video insights, not image PII redaction.
Trap 3: Azure Cognitive Search.
Search does not process images.
- A
Azure Form Recognizer.
Why wrong: Extracts data from forms, not general PII redaction.
- B
Azure Video Analyzer for Media.
Why wrong: Primarily for video insights, not image PII redaction.
- C
Azure AI Content Safety.
Can detect and redact sensitive content.
- D
Azure Computer Vision (OCR).
OCR extracts text; can be used to detect PII.
- E
Azure Cognitive Search.
Why wrong: Search does not process images.
A security company uses Azure Video Analyzer on IoT Edge to detect intrusions. The edge device has limited compute and network. They need to reduce latency. What should they configure?
Trap 1: Increase the video resolution sent to the cloud.
Higher resolution increases latency.
Trap 2: Enable cloud-based processing for all frames.
Adds network latency.
Trap 3: Use multiple AI models simultaneously.
Increases compute load.
- A
Increase the video resolution sent to the cloud.
Why wrong: Higher resolution increases latency.
- B
Set a high minimum confidence threshold for detection.
Filters out low-confidence results, saving compute.
- C
Enable cloud-based processing for all frames.
Why wrong: Adds network latency.
- D
Use multiple AI models simultaneously.
Why wrong: Increases compute load.
A company uses Azure Face API to detect faces in a crowd. They need to comply with GDPR and delete face data after 30 days. What should they implement?
Trap 1: Enable data encryption at rest.
Encryption does not delete data.
Trap 2: Set a retention policy on the Face API resource.
No such policy exists for Face API.
Trap 3: Recreate the Face API resource every 30 days.
Unnecessary and causes service disruption.
- A
Enable data encryption at rest.
Why wrong: Encryption does not delete data.
- B
Set a retention policy on the Face API resource.
Why wrong: No such policy exists for Face API.
- C
Use the Face API Delete operation to remove stored face IDs.
Explicitly deleting face data meets GDPR requirements.
- D
Recreate the Face API resource every 30 days.
Why wrong: Unnecessary and causes service disruption.
A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?
Trap 1: Deploy the bot code to an Azure Function.
Azure Functions is not mandatory; other hosting options exist.
Trap 2: Write the bot in C# using Azure SDK for .NET.
Bot can be written in multiple languages.
- A
Use the Bot Framework SDK to build the bot with Teams-specific features.
SDK provides Teams integration capabilities.
- B
Create a Teams app manifest file with bot configuration.
Manifest defines the bot's identity in Teams.
- C
Register the bot in the Azure portal and obtain a Microsoft App ID.
Registration is required for authentication.
- D
Deploy the bot code to an Azure Function.
Why wrong: Azure Functions is not mandatory; other hosting options exist.
- E
Write the bot in C# using Azure SDK for .NET.
Why wrong: Bot can be written in multiple languages.
A company wants to use Azure AI Translator to translate customer emails from English to French. They need to ensure that the translation preserves the tone and formality of the original text. What should they configure in the request?
Trap 1: Set the 'category' parameter to 'general' to use a standard…
Category is for custom models, not formality.
Trap 2: Set the 'scope' parameter to 'document' to ensure context-aware…
There is no 'scope' parameter in Azure AI Translator.
Trap 3: Set the 'language' parameter to 'fr' and the 'from' parameter to…
These are required but do not control formality.
- A
Set the 'category' parameter to 'general' to use a standard translation model.
Why wrong: Category is for custom models, not formality.
- B
Set the 'scope' parameter to 'document' to ensure context-aware translation.
Why wrong: There is no 'scope' parameter in Azure AI Translator.
- C
Set the 'formality' parameter to the desired level (e.g., 'formal' or 'informal').
The formality parameter controls the tone of the translation.
- D
Set the 'language' parameter to 'fr' and the 'from' parameter to 'en'.
Why wrong: These are required but do not control formality.
A financial services company is building an agent that uses Azure OpenAI to generate investment advice. The agent must be monitored for toxicity and bias. Which combination of services should the team use to implement content safety monitoring?
Trap 1: Azure Cognitive Search and Azure AI Language.
These do not provide content safety monitoring.
Trap 2: Azure Bot Service and Azure Logic Apps.
These do not include content safety features.
Trap 3: Azure Machine Learning and Azure Functions.
These are not designed for real-time content safety.
- A
Azure Cognitive Search and Azure AI Language.
Why wrong: These do not provide content safety monitoring.
- B
Azure Bot Service and Azure Logic Apps.
Why wrong: These do not include content safety features.
- C
Azure Machine Learning and Azure Functions.
Why wrong: These are not designed for real-time content safety.
- D
Azure AI Content Safety and Azure OpenAI content filtering.
These services provide comprehensive content safety.
An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?
Trap 1: The temperature parameter is set too high.
Temperature does not affect function calling capability.
Trap 2: The model version is outdated.
Outdated versions still support function calling.
- A
The temperature parameter is set too high.
Why wrong: Temperature does not affect function calling capability.
- B
The token limit is too low, truncating the function definitions.
Low token limits can cut off function definitions.
- C
The function parameter schemas are incorrect or incomplete.
Incorrect schemas prevent proper function execution.
- D
The function descriptions are ambiguous or missing.
Ambiguous descriptions can confuse the model.
- E
The model version is outdated.
Why wrong: Outdated versions still support function calling.
A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?
Trap 1: Microsoft Bot Framework SDK with a custom connector.
Bot Framework does not inherently provide compliance.
Trap 2: Azure Cognitive Search with custom analyzers.
Cognitive Search is not a platform for building agents.
Trap 3: Azure OpenAI Service with data processing enabled.
Azure OpenAI Service may not be HIPAA compliant in all regions.
- A
Microsoft Bot Framework SDK with a custom connector.
Why wrong: Bot Framework does not inherently provide compliance.
- B
Azure Cognitive Search with custom analyzers.
Why wrong: Cognitive Search is not a platform for building agents.
- C
Azure OpenAI Service with data processing enabled.
Why wrong: Azure OpenAI Service may not be HIPAA compliant in all regions.
- D
Azure AI Services deployed in a private endpoint with no data leaving the network.
Private endpoint ensures data stays within the network, aiding compliance.
A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?
Trap 1: Fine-tune the model on a dataset of correct function calls.
Fine-tuning may not be necessary and is expensive.
Trap 2: Reduce the number of functions to only the most common ones.
This may not cover all required tasks.
Trap 3: Set the temperature parameter to 0 for deterministic output.
Temperature affects creativity, not function selection accuracy.
- A
Fine-tune the model on a dataset of correct function calls.
Why wrong: Fine-tuning may not be necessary and is expensive.
- B
Reduce the number of functions to only the most common ones.
Why wrong: This may not cover all required tasks.
- C
Set the temperature parameter to 0 for deterministic output.
Why wrong: Temperature affects creativity, not function selection accuracy.
- D
Provide better function descriptions with examples of when to use each function.
Clear descriptions improve function selection.
A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?
Trap 1: Use a different OCR API from Azure Cognitive Services.
All Azure OCR APIs have similar limitations on image quality.
Trap 2: Train a custom OCR model using Azure Custom Vision.
Custom Vision is for object detection, not OCR.
Trap 3: Increase the confidence threshold for text detection.
This does not improve recognition; it only filters low-confidence results.
- A
Use a different OCR API from Azure Cognitive Services.
Why wrong: All Azure OCR APIs have similar limitations on image quality.
- B
Pre-process the image to adjust contrast and brightness before calling the OCR API.
Image pre-processing improves OCR accuracy.
- C
Train a custom OCR model using Azure Custom Vision.
Why wrong: Custom Vision is for object detection, not OCR.
- D
Increase the confidence threshold for text detection.
Why wrong: This does not improve recognition; it only filters low-confidence results.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.