AI-102 Plan and manage an Azure AI solution Practice Test 9 — 15 Questions

Question 1

A healthcare company is using Azure AI Document Intelligence to extract patient data from forms. They need to ensure that all extracted data is encrypted at rest using a customer-managed key (CMK) and that the service endpoint is restricted to a specific virtual network. Which combination of steps should they take?

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

Accepted Answer

Create a private endpoint and associate a customer-managed key in the resource encryption settings. It combines a private endpoint (which restricts the service endpoint to a specific virtual network by providing a private IP address within that VNet, eliminating public internet exposure) with a customer-managed key (CMK) in the resource encryption settings, which ensures data at rest is encrypted using a key stored in Azure Key Vault that the customer controls. This directly meets both requirements: network isolation via private endpoint and CMK-based encryption at rest.

Answer

Use a service endpoint and configure a managed identity

Answer

Disable public network access and enable CMK via Azure Key Vault

Answer

Configure IP firewall rules and enable CMK via Azure Key Vault

Question 2

You are deploying a custom Azure AI Language question answering project. The solution must only answer questions based on a specific set of internal FAQ documents. Which data source type should you use when creating the project?

Accepted Answer

URLs or files containing FAQ content. Azure AI Language custom question answering is designed to ingest structured FAQ content from URLs or files. When you create a custom project, selecting 'URLs or files containing FAQ content' as the data source type allows the service to automatically extract question-answer pairs from the provided documents, ensuring the solution only answers questions based on that specific set of internal FAQs.

Answer

Prebuilt model from Azure AI Language

Answer

Azure SQL Database with a QnA Maker schema

Answer

Azure Cognitive Search index

Question 3

Your team is building an AI-powered search application using Azure AI Search with semantic ranking. During testing, the search results are not showing the expected relevance improvements. What should you check first?

Accepted Answer

Confirm that the search service is at least Standard S1 and semantic ranking is enabled. Semantic ranking in Azure AI Search requires a service tier of at least Standard S1 (or higher) and the feature must be explicitly enabled on the index. Without meeting these prerequisites, the search results will not benefit from semantic reranking, regardless of other configurations. Therefore, confirming the service tier and semantic ranking enablement is the first and most critical check.

Answer

Verify that CORS is enabled for your application

Answer

Check that your index has an RBAC role assignment for semantic search

Answer

Ensure the search service is on the Free tier

Question 4

You are responsible for managing costs for multiple Azure AI services in your organization. You notice that provisioned throughput units (PTUs) for Azure OpenAI are not fully utilized. What is the most cost-effective action to optimize spending?

Accepted Answer

Reduce the number of provisioned PTUs or switch to pay-as-you-go. Provisioned throughput units (PTUs) represent a fixed capacity commitment. If PTUs are underutilized, the most cost-effective action is to reduce the number of PTUs or switch to the pay-as-you-go (PAYG) model, which charges only for tokens consumed. This directly aligns with cost optimization by eliminating the fixed cost of unused capacity.

Answer

Configure auto-scaling to reduce PTUs during low usage

Answer

Move the resource to a different region with lower pricing

Answer

Stop the Azure OpenAI service when not in use

Question 5

You are using Azure AI Content Safety to moderate user-generated content in a social media app. The solution must detect and block hate speech and self-harm content in real time. Which Content Safety feature should you use?

Accepted Answer

Text moderation. Text moderation is the correct choice because Azure AI Content Safety's text moderation API is specifically designed to detect and block hate speech and self-harm content in real time. It analyzes text for severity levels across multiple categories, including hate and self-harm, and returns a severity score to trigger blocking actions. This directly meets the requirement for real-time detection of these specific content types in user-generated text.

Answer

Custom category management

Answer

Severity analysis

Answer

Image moderation

Question 6

A company is deploying a solution using Azure AI Vision to analyze images of products on a retail website. They need to ensure that the image analysis is performed within a specific geographic boundary for data residency compliance. What should they configure?

Accepted Answer

Deploy the Azure AI Vision resource in the desired Azure region. Azure AI Vision resources are regional Azure resources, meaning all data processing and storage occur within the Azure region where the resource is deployed. By deploying the resource in the desired geographic region, you ensure that image analysis and any derived data remain within that boundary, satisfying data residency compliance requirements. This is the fundamental mechanism for controlling data location in Azure AI services.

Answer

Create a private endpoint for the Vision resource

Answer

Enable multi-region replication on the Vision resource

Answer

Use the Free tier of Azure AI Vision

Question 7

You are developing a bot using Microsoft Bot Framework and Azure AI Language. The bot must handle user intents that change mid-conversation. Which feature should you implement?

Accepted Answer

Adaptive dialogs. Adaptive dialogs are designed for dynamic, event-driven conversations where user intents can change mid-conversation. They use a trigger-based model (e.g., onIntent, onTurn) that allows the bot to react to new intents at any point, unlike linear dialog models. This makes them ideal for handling mid-conversation intent shifts without requiring predefined dialog flows.

Answer

Prompt dialogs

Answer

Waterfall dialogs

Answer

QnA Maker knowledge base

Question 8

Your organization needs to monitor Azure AI services for unusual activity patterns that might indicate a security threat. Which Microsoft security solution should you use?

Accepted Answer

Microsoft Sentinel. Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration Automated Response (SOAR) solution. It is specifically designed to ingest logs from Azure AI services, apply analytics to detect unusual activity patterns, and generate alerts for potential security threats, making it the correct choice for monitoring AI services for security anomalies.

Answer

Microsoft Intune

Answer

Microsoft Defender XDR

Answer

Microsoft Purview

Question 9

Which TWO actions are recommended to secure an Azure AI Language resource?

Accepted Answer

Use managed identities for authentication from Azure services. Managed identities allow Azure AI Language resources to authenticate to other Azure services (like Azure Storage or Azure Key Vault) without storing credentials in code or configuration. This eliminates the risk of access key leakage and aligns with the principle of least privilege, as the identity is tied to the resource lifecycle and can be granted granular RBAC permissions.

Answer

Enable local authentication with access keys

Answer

Allow all Azure services through the firewall

Answer

Enable anonymous access for public APIs

Question 10

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle high throughput and process documents in batch. Which TWO configuration options should you use?

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. The standard (S0) tier is required for production-grade throughput because it offers higher transactions per second (TPS) and better scalability compared to the free (F0) tier. For batch processing of invoices, the async document analysis API is the correct choice because it supports submitting multiple documents in a single call and returns results via a polling mechanism, which is designed for high-volume scenarios.

Answer

Use the synchronous API for each invoice

Answer

Train a custom model for invoice extraction

Answer

Store output directly in Azure Blob Storage without API calls

Question 11

A company is planning to use Azure AI services. They require the ability to audit all API calls for compliance. Which THREE components should they enable?

Accepted Answer

Azure Monitor. Azure Monitor is correct because it provides a centralized platform for collecting, analyzing, and acting on telemetry data from Azure resources, including API call logs. By enabling diagnostic settings for the AI service, you can route audit logs (such as all REST API requests and responses) to Azure Monitor, which then integrates with Log Analytics for querying and alerting. This triad—Azure Monitor, Log Analytics workspace, and diagnostic settings—forms the complete pipeline required to audit all API calls for compliance.

Answer

Azure RBAC roles for the service

Answer

Managed identity for the service

Question 12

You see the exhibit in an Azure AI Search skillset definition. The custom skill is not executing as expected. What is the most likely cause?

Accepted Answer

The skill is missing the 'uri' property to specify the endpoint. The custom skill is not executing because the 'uri' property, which specifies the endpoint URL for the web API, is missing from the skill definition. Azure AI Search requires this property to route requests to the external service; without it, the skill cannot be invoked and will fail silently or return errors.

Answer

The input source path is incorrect

Answer

The JSON is malformed and will not parse

Answer

The type should be 'Microsoft.Skills.Custom.WebApiSkill' with dots

Question 13

You see the exhibit representing an Azure Bot resource configuration. The bot is not responding to user messages. What should you verify first?

Accepted Answer

Check that the endpoint URL is correct and the web app is running. The most common reason a bot fails to respond to user messages is that the endpoint URL configured in the Azure Bot resource does not match the actual URL of the running web app, or the web app itself is stopped or unresponsive. Without a correct and reachable endpoint, the Bot Framework Service cannot forward messages to the bot's message handler, causing a complete communication breakdown.

Answer

Ensure that LUIS app IDs are provided

Answer

Verify that the Application Insights key is valid

Answer

Confirm that the msaAppId is a valid GUID

Question 14

You see the exhibit from an Azure OpenAI chat completion request. The assistant is not calling the get_weather function when asked about the weather. What is the most likely reason?

Accepted Answer

The function is defined in the system message but not in the 'tools' array. D is correct because in Azure OpenAI, functions must be defined in the 'tools' array of the chat completion request to be available for the model to call. Defining a function only in the system message does not register it as a callable tool; the model can see the description but cannot invoke it. Without the function in 'tools', the assistant will ignore the request to call get_weather.

Answer

The required parameter 'location' is missing from the user message

Answer

Function calling is not supported in the current Azure OpenAI version

Answer

The function definition is malformed

Question 15

You are deploying an Azure AI Language service custom text classification model. After training, the model achieves 95% accuracy on the test set but only 60% on a held-out validation set. What is the most likely cause?

Accepted Answer

Overfitting to the training data. A 95% accuracy on the test set but only 60% on a held-out validation set is a classic sign of overfitting. The model has memorized patterns specific to the training and test sets (which may share distribution or preprocessing artifacts) but fails to generalize to unseen data. In Azure AI Language custom text classification, overfitting often occurs when the model is too complex relative to the amount of training data or when hyperparameters like learning rate or number of epochs are not tuned properly.

Answer

Data leakage between training and test sets

Answer

Insufficient training data

Answer

Label imbalance in the training data