AI-102 Practice Test 24 — 15 Questions

Question 1

A healthcare organization uses Azure AI Vision to analyze medical images for abnormalities. The solution must ensure patient data is not stored in the service's logs. What should you configure?

Accepted Answer

Set the logging level to 'None' in the Azure AI Vision resource diagnostic settings.. Setting the logging level to 'None' in the Azure AI Vision resource diagnostic settings prevents any patient data from being captured in the service's logs. This directly addresses the requirement to ensure patient data is not stored, as diagnostic logs can contain request and response payloads that include medical image metadata or analysis results.

Answer

Use a private endpoint to access the Azure AI Vision resource.

Answer

Enable Customer-Managed Keys (CMK) for the resource.

Answer

Use Azure Policy to restrict data residency.

Question 2

You are designing a solution that uses Azure OpenAI Service to generate product descriptions based on product attributes (name, category, features). The solution must: - Use a GPT-4 model deployed in the West US region. - Implement content filtering to block inappropriate content. - Handle up to 100 requests per second. - Minimize latency. - Use managed identity for authentication. What should you include in the design?

Accepted Answer

Deploy a single GPT-4 model with sufficient capacity (e.g., 100K TPM). Enable content filtering. Use a system-assigned managed identity for authentication.. Deploying a single GPT-4 model with sufficient capacity (e.g., 100K TPM) ensures the solution can handle up to 100 requests per second while minimizing latency by avoiding cross-region calls. Enabling content filtering directly on the Azure OpenAI deployment blocks inappropriate content without additional services, and using a system-assigned managed identity provides secure, keyless authentication that aligns with Azure best practices.

Answer

Deploy multiple GPT-4 models across different regions to reduce latency.

Answer

Use API key authentication stored in Azure Key Vault. Deploy two GPT-4 models to load balance requests.

Answer

Use Azure AI Content Safety in addition to Azure OpenAI to filter content.

Question 3

You are implementing a knowledge mining solution for a legal firm. The solution must ingest large volumes of legal documents (PDFs and Word files) stored in Azure Blob Storage. You need to extract text, recognize named entities (e.g., parties, judges, case numbers), and index the content for full-text search. The solution should also support redaction of sensitive information before indexing. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence, Azure AI Language, custom skill for redaction, and Azure AI Search. It combines Azure AI Document Intelligence for OCR and text extraction from PDFs and Word files, Azure AI Language for named entity recognition (e.g., parties, judges, case numbers), a custom skill for redaction (to remove sensitive information before indexing), and Azure AI Search to index the cleaned content for full-text search. This stack directly addresses all requirements: ingestion, entity extraction, redaction, and search indexing.

Answer

Azure AI Document Intelligence, Azure AI Translator, and Azure AI Search

Answer

Azure AI Document Intelligence, Azure AI Video Indexer, and Azure AI Search

Answer

Azure AI Document Intelligence, Azure AI Content Safety, and Azure AI Search

Question 4

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?

Accepted Answer

Provide better function descriptions with examples of when to use each function.. Providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.

Answer

Fine-tune the model on a dataset of correct function calls.

Answer

Reduce the number of functions to only the most common ones.

Answer

Set the temperature parameter to 0 for deterministic output.

Question 5

Which THREE factors should you consider when choosing between Azure OpenAI Service and Azure Machine Learning for deploying a generative AI model?

Accepted Answer

Latency requirements: Azure OpenAI may offer lower latency for standard models.. Azure OpenAI Service provides managed endpoints for pre-trained models like GPT-4, which are optimized for low-latency inference out of the box. In contrast, Azure Machine Learning requires you to deploy your own containerized model, which can introduce additional network and compute overhead, making Azure OpenAI the better choice when sub-100ms response times are critical for standard generative AI tasks.

Answer

Integration with Microsoft Purview for data governance.

Answer

Ability to scale to thousands of concurrent requests.

Question 6

You are deploying a generative AI model using Azure Machine Learning. The model is a large language model that requires GPU compute. You need to minimize costs while ensuring the model is always available for inference. Which compute option should you choose?

Accepted Answer

Managed online endpoint with a GPU VM. A managed online endpoint with a GPU VM is the correct choice because it provisions a dedicated GPU instance that remains always active, ensuring the model is available for real-time inference at any time. This option balances cost and availability by allowing you to choose a lower-cost GPU SKU (e.g., NCas_v4) while avoiding the cold-start latency of serverless or batch options. The managed endpoint also handles auto-scaling and load balancing, but for constant availability, a fixed minimum instance count is required.

Answer

Batch endpoint with GPU

Answer

Serverless GPU compute

Answer

CPU-based inference

Question 7

A retail company uses Azure AI Vision to analyze shelf images for inventory management. They notice that the Object Detection model sometimes misses small items. What is the most effective way to improve detection of small objects?

Accepted Answer

Train a custom object detection model with annotated images that include small objects.. Training a custom object detection model with annotated images that include small objects directly improves the model's ability to detect them. Option A is wrong because preprocessing to remove background noise does not specifically target small object detection; the model may still miss small items. Option C is wrong because the Background Removal API is used for isolating items from the background, not for improving detection accuracy. Option D is wrong although higher resolution can help, it is not as effective as training a custom model with properly annotated small objects, and it may increase cost and latency.

Answer

Preprocess images to remove background noise.

Answer

Use the Background Removal API to isolate items.

Answer

Increase the image resolution before sending to the API.

Question 8

Drag and drop the steps to build and deploy a custom Azure AI Document Intelligence model into the correct order.

Accepted Answer

Step 1: Gather and label documents, Step 2: Create the Azure AI Document Intelligence resource, Step 3: Train the model, Step 4: Test the model, Step 5: Publish the model. First gather and label documents, create the resource, train the model, test, and publish.

Answer

Step 1: Create the Azure AI Document Intelligence resource, Step 2: Gather and label documents, Step 3: Train the model, Step 4: Test the model, Step 5: Publish the model

Answer

Step 1: Gather and label documents, Step 2: Train the model, Step 3: Create the Azure AI Document Intelligence resource, Step 4: Test the model, Step 5: Publish the model

Answer

Step 1: Gather and label documents, Step 2: Create the Azure AI Document Intelligence resource, Step 3: Test the model, Step 4: Train the model, Step 5: Publish the model

Question 9

Your company wants to moderate user-uploaded images for adult content. Which Azure AI service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and moderate inappropriate content, including adult content, in images and text. It provides severity-based classifications (safe, low, medium, high) for categories such as hate, self-harm, sexual, and violence, making it ideal for user-uploaded image moderation.

Answer

Azure AI Face

Answer

Azure AI Document Intelligence

Answer

Azure AI Vision Image Analysis

Question 10

Drag and drop the steps to configure an Azure AI Vision custom image classification model into the correct order.

Accepted Answer

Step 1: Prepare labeled images, Step 2: Create Azure AI Vision resource, Step 3: Train model, Step 4: Evaluate model, Step 5: Publish model. The correct sequence is to start with labeled images, then create the Azure AI Vision resource, train the model, evaluate its performance, and finally publish it. This ensures that all prerequisites are met and the model is validated before deployment.

Answer

Step 1: Create Azure AI Vision resource, Step 2: Prepare labeled images, Step 3: Train model, Step 4: Evaluate model, Step 5: Publish model

Answer

Step 1: Prepare labeled images, Step 2: Train model, Step 3: Evaluate model, Step 4: Create Azure AI Vision resource, Step 5: Publish model

Answer

Step 1: Prepare labeled images, Step 2: Train model, Step 3: Create Azure AI Vision resource, Step 4: Evaluate model, Step 5: Publish model

Question 11

You are developing a chat application that uses Azure OpenAI Service to answer customer queries. The solution must ensure that the model does not generate harmful or offensive content. Which Azure AI service should you configure?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is specifically designed to detect and filter harmful or offensive content in text and images, making it the appropriate service to integrate with an Azure OpenAI chat application to enforce content safety policies. It provides APIs for content moderation, including severity-based filtering for hate, self-harm, sexual, and violence categories, which directly addresses the requirement to prevent the model from generating harmful output.

Answer

Azure AI Bot Service

Answer

Azure AI Search

Answer

Azure AI Language

Question 12

A company uses Azure Content Moderator to moderate user-generated content. They need to ensure that content moderation workflows comply with regional regulations. Which TWO actions should they take?

Accepted Answer

Deploy Content Moderator resources in the required geographic regions to meet data residency requirements.. Deploying Azure Content Moderator resources in the required geographic regions ensures that user-generated content is processed and stored within specific data boundaries, directly addressing data residency regulations. This is a fundamental compliance requirement because Azure resources are region-bound, and data does not leave the selected region unless explicitly configured otherwise.

Answer

Use the free tier to reduce costs while meeting compliance needs.

Answer

Enable geo-tagging on the Content Moderator API to automatically apply region-specific moderation.

Answer

Configure the API to automatically reject any content that violates regional laws.

Question 13

Refer to the exhibit. You run the Azure CLI command shown. What is the output?

Accepted Answer

A single key string. The `az cognitiveservices account keys list` command retrieves the access keys for an Azure Cognitive Services account. By default, it returns both key1 and key2, but the `--query` parameter with `keys[0]` filters the output to return only the first key (key1) as a single string. This is a standard Azure CLI query syntax using JMESPath to extract a specific element from the JSON response.

Answer

Both key1 and key2

Answer

The endpoint URL

Answer

The region of the account

Question 14

Which TWO features are available in Azure Computer Vision but not in Azure Custom Vision?

Accepted Answer

Optical character recognition (OCR). Optical Character Recognition (OCR) and image captioning are features available in Azure Computer Vision but not in Azure Custom Vision. Azure Computer Vision provides pre-built OCR APIs (e.g., Read API) for extracting text from images and captioning models for generating human-readable descriptions. Azure Custom Vision is focused on training custom models for image classification and object detection, and does not include built-in OCR or captioning capabilities.

Answer

Object detection

Answer

Image classification

Answer

Face detection

Question 15

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.