AI-102 Plan and manage an Azure AI solution Practice Test 14 — 15 Questions

Question 1

Your Azure AI Search indexer is failing to index documents from an Azure Blob Storage container. The error message shows 'AccessDenied'. What is the most likely cause?

Accepted Answer

The search service's managed identity lacks 'Storage Blob Data Reader' role.. The 'AccessDenied' error indicates that the Azure AI Search service lacks the necessary permissions to read data from the Azure Blob Storage container. The most likely fix is to assign the 'Storage Blob Data Reader' role to the search service's system-assigned managed identity, which grants read access to blob containers and their contents.

Answer

The blob container name is misspelled in the datasource.

Answer

The indexer execution interval is set too high.

Answer

The index schema does not match the blob metadata.

Question 2

Which THREE factors should you consider when choosing between Azure AI Document Intelligence prebuilt models and custom models for invoice processing?

Accepted Answer

Prebuilt models require no training data.. Option B is correct because Azure AI Document Intelligence prebuilt invoice models are pretrained by Microsoft and can be invoked immediately without supplying any labeled training data, which is ideal when you want fast time-to-value on standard invoices. Option D is correct because custom models (template or neural) are trained on your own labeled invoice samples, and although the exact count varies by model type, a meaningful set of labeled invoices is required to teach the model your specific fields and layouts. Option E is correct because custom models are specifically designed to handle non-standard, supplier-specific, or unusual invoice layouts that prebuilt models may not parse accurately. Option A is not correct because these are Azure cloud services accessed via the Document Intelligence endpoint/API, not on-premises deployable models. Option C is not correct because prebuilt models are not always less accurate than custom models; for standard invoice formats they can perform very well, and accuracy depends on the document set and training quality.

Answer

Both model types can be deployed on-premises.

Answer

Prebuilt models are always less accurate than custom models.

Question 3

Refer to the exhibit. You are using Azure AI Document Intelligence with a layout model. The pipeline returns an empty tables array even though the document contains tables. The OCR step extracts text correctly. What is the most likely issue?

Accepted Answer

The layout extraction step is not correctly identifying table structures.. The layout model in Azure AI Document Intelligence performs OCR and then uses a layout extraction step to identify structural elements like tables. If the OCR extracts text correctly but the tables array is empty, it indicates that the layout extraction step failed to detect the table boundaries or cell structure, not that OCR missed the text. Option D correctly identifies this as the most likely issue.

Answer

The OCR step is not recognizing table cells.

Answer

The table extraction step is misconfigured.

Answer

The output field mapping for tables is missing.

Question 4

Your company uses Azure AI Vision to analyze images. You receive an alert that the number of 429 (Too Many Requests) errors has increased significantly. What is the most likely cause?

Accepted Answer

The application is exceeding the transactions-per-second (TPS) limit.. HTTP 429 (Too Many Requests) is a rate-limiting response from Azure AI Vision when the client exceeds the allowed transactions-per-second (TPS) for the chosen pricing tier. The alert indicates the application is sending requests faster than the service's capacity, triggering throttling to protect backend resources.

Answer

The endpoint URL is incorrect.

Answer

The API key has expired.

Answer

The service principal does not have the correct role assignment.

Question 5

Your Azure AI Search index is experiencing high query latency. You have enabled semantic search and custom scoring profiles. You need to reduce latency without degrading search quality. Which action should you take?

Accepted Answer

Increase the number of replicas.. Increasing the number of replicas distributes query load across multiple copies of the index, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.

Answer

Remove custom scoring profiles.

Answer

Reduce the number of partitions.

Answer

Disable semantic search.

Question 6

Your organization is using Azure OpenAI Service to generate content. You need to ensure that the content meets safety guidelines by filtering harmful outputs. What should you configure?

Accepted Answer

Configure the content filters in the Azure OpenAI Studio.. Content filters in Azure OpenAI Studio allow you to define severity levels (safe, low, medium, high) for categories like hate, sexual, violence, and self-harm, which are enforced at the inference API level to block or flag harmful outputs before they reach the user. This is the primary configuration for filtering model-generated content in Azure OpenAI Service.

Answer

Enable the Responsible AI dashboard.

Answer

Use Azure AI Content Safety APIs to analyze outputs.

Answer

Set the system message to instruct the model to avoid harmful content.

Question 7

Which THREE components are part of the Azure AI Bot Service?

Accepted Answer

Azure Bot Service (hosting). Azure Bot Service (hosting) (B) is correct because the Azure AI Bot Service provides the hosting/registration resource that exposes a messaging endpoint and connects bots to channels such as Microsoft Teams, Web Chat, and Direct Line. Bot Framework SDK (D) is correct because it is the core development library (for C#, JavaScript/TypeScript, Python, and Java) used to build bots that handle activities, turns, dialogs, and channel connectors. Bot Framework Composer (E) is correct because it is the visual authoring tool included in the Bot Framework tooling for designing, testing, and publishing conversational bots. Azure AI Search (A) is not part of the Bot Service; it is a separate cognitive search service for indexing and querying content. Azure AI Language (CLU) (C) is also not part of the Bot Service; it is a separate Azure AI Language capability for conversational language understanding that a bot may call but that is not a Bot Service component.

Answer

Azure AI Search

Answer

Azure AI Language (CLU)

Question 8

You are deploying an Azure AI Language service solution for a multilingual customer support chatbot. The solution must support real-time translation between English, Spanish, and French. Which Azure resource should you provision?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct resource because it provides real-time text translation across multiple languages, including English, Spanish, and French, via a dedicated REST API. The scenario specifically requires translation between languages, not speech recognition or generative AI, making Azure AI Translator the precise service for this task.

Answer

Azure AI Speech

Answer

Azure OpenAI Service

Answer

Azure AI Language

Question 9

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?

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 10

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 11

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 12

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 13

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 14

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 15

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