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

Question 1

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?

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

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. Option C is correct because the standard (S0) tier is the production-grade Document Intelligence pricing tier that supports high request volumes and higher transactions-per-second throughput, whereas the free (F0) tier is limited to low-volume testing and would throttle a high-throughput batch workload. Option D is correct because the asynchronous document analysis API (e.g., POST to /documentModels/{modelId}:analyze followed by polling the Operation-Location header) is specifically designed for large-scale, batch processing of many documents without blocking on each request, which is required for high throughput. Option A is wrong because the synchronous API processes one document per blocking call and does not scale well for batch, high-throughput scenarios. Option B is wrong because training a custom model addresses extraction accuracy for specialized document layouts, not throughput or batch processing capability. Option E is wrong because storing output in Blob Storage without API calls bypasses Document Intelligence entirely, so no extraction would occur.

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 2

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 (A) is correct because it is the platform that collects, stores, and surfaces activity and resource logs, enabling the audit trail of API calls required for compliance. A Log Analytics workspace (B) is correct because it is the destination where diagnostic logs are ingested and queried with KQL, allowing retention and analysis of all API call records. Diagnostic settings for the AI service (C) is correct because it is the mechanism that routes the service's resource logs (e.g., Audit, RequestResponse) and metrics to Azure Monitor/Log Analytics, which is what actually captures the API calls. Azure RBAC roles (D) only control authorization to the resource and do not record API call history, and a managed identity (E) only provides an authentication credential for the service, so neither produces the audit data required.

Answer

Azure RBAC roles for the service

Answer

Managed identity for the service

Question 3

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 4

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 5

Your Azure AI Search index stores customer support tickets. You need to implement a search feature that returns semantically similar results even if the query uses different wording. Which configuration should you enable?

Accepted Answer

Enable semantic search and configure a semantic configuration. Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than keyword matching.

Answer

Use simple query parsing with searchMode=any

Answer

Add a synonym map with custom entries

Answer

Enable fuzzy search on the index

Question 6

You are building a chatbot using Azure AI Bot Service and Language Service. The bot must recognize user intent for 'check order status'. How should you configure the Language Service?

Accepted Answer

Create a custom intent classification project. To recognize user intent for 'check order status', you need a custom intent classification project in Azure Language Service. This project type uses a trained model to map utterances to specific intents, such as 'CheckOrderStatus', which is exactly what the chatbot requires. Prebuilt models or QnA Maker do not provide custom intent recognition.

Answer

Deploy a QnA Maker knowledge base

Answer

Configure a sentiment analysis endpoint

Answer

Use the prebuilt entity extraction model

Question 7

You need to analyze images to detect objects and read text from documents using a single Azure AI service. Which service should you use?

Accepted Answer

Azure AI Vision. Azure AI Vision is the correct choice because it provides both image analysis (object detection) and optical character recognition (OCR) for reading text from documents within a single service. Its Read API and Analyze Image API cover both requirements without needing separate services.

Answer

Azure AI Document Intelligence (Form Recognizer)

Answer

Azure AI Custom Vision

Answer

Azure AI Language Service

Question 8

Your Azure AI Language custom entity recognition model incorrectly extracts 'Microsoft' as an organization when it refers to the company, but fails to extract 'Microsoft' as a product when it refers to the software. How should you improve the model?

Accepted Answer

Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context. Custom entity recognition models in Azure AI Language learn to distinguish entity types based on context. By labeling 'Microsoft' as 'Organization' in sentences where it refers to the company and as 'Product' in sentences where it refers to the software, you provide the model with the contextual clues needed to disambiguate the same token across different uses. This supervised learning approach directly addresses the model's failure to recognize the product entity.

Answer

Reduce the amount of training data to avoid confusion

Answer

Remove the 'Organization' entity type from the model

Answer

Add more training sentences without labeling the entity type

Question 9

You need to create a solution that extracts key-value pairs from scanned invoices using Azure AI Document Intelligence. The invoices have varying layouts. Which model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model (Option D) is specifically designed to extract key-value pairs, line items, and other structured fields from invoices, even when layouts vary. It is trained on thousands of invoice samples and uses deep learning to handle diverse formats without requiring custom training, making it the correct choice for this scenario.

Answer

Layout model

Answer

Custom extraction model

Answer

Read model

Question 10

Your Azure AI Search index contains millions of documents. Users report that search results are slow for complex queries. You need to improve query performance without reducing result quality. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas in Azure AI Search distributes query load across multiple copies of the index, enabling parallel processing of complex queries. This directly improves query throughput and latency without altering the index schema or reducing result quality, as replicas provide dedicated resources for query execution.

Answer

Reduce the maximum number of results returned per query

Answer

Remove all facet fields from the index

Answer

Disable complex query types such as fuzzy and regex

Question 11

Which THREE of the following are capabilities of Azure AI Content Safety?

Accepted Answer

Sexual content detection. Azure AI Content Safety provides built-in AI classifiers that detect harmful content across four categories: sexual, hate, violence, and self-harm, so options A (sexual content detection), B (hate speech detection), and C (self-harm detection) are all correct capabilities of the service. These categories are exposed through the Analyze Text and Analyze Image APIs, which return severity scores (0-7) for each category, enabling applications to filter or moderate harmful content. Option D (sentiment analysis) is not part of Content Safety; sentiment analysis is a feature of Azure AI Language. Option E (PII detection) is also not part of Content Safety; PII detection is provided by Azure AI Language's Personally Identifiable Information extraction capability.

Answer

Sentiment analysis

Answer

Personally identifiable information (PII) detection

Question 12

Which TWO of the following are best practices for managing Azure AI services costs?

Accepted Answer

Use the S0 pricing tier for production workloads. Option A is correct because the S0 (Standard) tier is the production-grade pricing tier for Azure AI services, offering higher throughput, SLA-backed availability, and pay-as-you-go billing that lets you match capacity to actual usage rather than being capped by Free-tier limits. Option E is correct because configuring budget alerts in Azure Cost Management (Microsoft Cost Management + Billing) proactively notifies you when spending approaches or exceeds defined thresholds, enabling early corrective action before costs escalate. Option B is incorrect because the Free tier (F0) has strict transaction and rate limits and is intended only for trials and evaluation, not production workloads. Option C is incorrect because scaling up partitions increases provisioned capacity and therefore cost, and it is a performance/scalability action rather than a cost-management best practice. Option D is incorrect because increasing batch size is a throughput optimization for supported batch APIs, not a general cost-control practice, and it does not reduce charges for services billed per transaction in the way implied.

Answer

Always use the Free tier to avoid charges

Answer

Scale up partitions to improve performance

Answer

Increase batch size to reduce number of API calls

Question 13

You are using Azure AI Language's conversational language understanding (CLU). The above JSON is a request to a CLU endpoint. What is the purpose of this request?

Accepted Answer

To predict the intent and entities from the user utterance. The JSON request is sent to the Azure AI Language CLU endpoint with a 'query' field containing the user utterance. The 'kind' field is set to 'Conversation', which triggers the CLU runtime to analyze the utterance against the deployed model. The purpose is to return a prediction of the top intent and any extracted entities, which is the core function of a conversational language understanding endpoint.

Answer

To query a knowledge base for answers

Answer

To deploy the CLU model to production

Answer

To train a new CLU model

Question 14

You run the above Azure CLI command. What is the expected output?

Accepted Answer

The primary and secondary keys. The Azure CLI command `az cognitiveservices account keys list` retrieves only the primary and secondary API keys for the Cognitive Services account. The endpoint is not included in the output; it must be obtained separately using `az cognitiveservices account show`. Therefore, the expected output contains the primary key and secondary key only.

Answer

The primary and secondary keys along with the endpoint

Answer

A list of endpoints for the service

Answer

An error because the command is incorrect

Question 15

Your company is deploying an Azure AI solution that uses multiple AI services. You need to ensure that all API calls are authenticated securely using managed identities. Which of the following steps is required to enable managed identity authentication for an Azure AI service?

Accepted Answer

Assign a managed identity to the Azure AI service and grant it the required RBAC role. Managed identity authentication for Azure AI services requires assigning either a system-assigned or user-assigned managed identity to the resource, and then granting that identity the appropriate RBAC role (e.g., Cognitive Services User) on the target AI service. This eliminates the need for keys or secrets in the code and leverages Microsoft Entra ID tokens for secure, passwordless authentication.

Answer

Enable system-assigned managed identity at the subscription level

Answer

Store the authentication key in Azure Key Vault and reference it in the application

Answer

Configure a shared access key in the Azure AI service