AI-102 Plan and manage an Azure AI solution Practice Test 13 — 15 Questions

Question 1

Refer to the exhibit. You are reviewing a content safety policy for an Azure AI Foundry deployment. The policy rate limits to 20 requests per minute. A user submits 50 requests in one minute. How many requests are allowed?

Accepted Answer

20. The content safety policy enforces a rate limit of 20 requests per minute. When a user submits 50 requests in one minute, the rate limiter allows only the first 20 requests and blocks the remaining 30. This is a standard token-bucket or sliding-window rate-limiting behavior in Azure AI Foundry, where exceeding the limit results in HTTP 429 (Too Many Requests) for excess requests.

Answer

50

Answer

100

Answer

None, all are blocked.

Question 2

You are designing a solution that uses Azure AI Language to analyze customer feedback. The solution must detect sentiment, extract key phrases, and identify named entities. Which feature should you use?

Accepted Answer

Azure AI Language service. The Azure AI Language service provides pre-built capabilities for sentiment analysis, key phrase extraction, and named entity recognition (NER) as part of its text analytics features. These three tasks are directly supported by the service's Analyze API, making it the correct choice for analyzing customer feedback text.

Answer

Azure AI Speech service

Answer

Azure AI Computer Vision

Answer

Translator API

Question 3

Your team uses Azure AI Foundry to deploy a custom chat model. The model must meet compliance by explaining its reasoning and citing sources. Which feature should you enable?

Accepted Answer

Groundedness detection with citation. Groundedness detection with citation is the correct feature because it directly addresses the compliance requirement for the model to explain its reasoning and cite sources. This feature, available in Azure AI Foundry, evaluates the model's responses against the provided grounding documents and automatically generates citations, ensuring that the output is factually supported and traceable.

Answer

Fine-tuning with domain-specific data

Answer

Content filtering

Answer

Prompt engineering

Question 4

You manage an Azure AI Search solution that indexes documents from Azure Blob Storage. The index must support real-time updates when documents are added or modified. Which approach should you use?

Accepted Answer

Configure an indexer with a short schedule and change tracking.. Option B is correct because Azure AI Search indexers can be configured with a short schedule (e.g., every 5 minutes) and change tracking (using high-water mark or integrated change detection on Azure Blob Storage) to automatically index new or modified documents without manual intervention. This provides near-real-time updates while leveraging the indexer's built-in change detection capabilities.

Answer

Use the push API to manually upload documents.

Answer

Manually reset and rerun the indexer after each change.

Answer

Add a cognitive skillset to process new documents.

Question 5

Your Azure AI solution uses multiple AI services including Computer Vision and Language. To reduce costs, you want to share a single key and endpoint across services. Which Azure resource type should you deploy?

Accepted Answer

Azure AI services multi-service resource. D is correct because Azure AI services multi-service resource provides a single endpoint and key that can be used across multiple AI services (e.g., Computer Vision, Language, Face, etc.), reducing management overhead and cost by consolidating billing. This resource type is designed specifically for scenarios where you want to share credentials across services without deploying separate single-service instances.

Answer

Separate single-service resources for each AI service

Answer

Azure Key Vault for storing keys

Answer

Azure API Management gateway

Question 6

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and data residency requirements. Which configuration is essential?

Accepted Answer

Create the AI resource in the required Azure region.. Data residency requirements dictate that the Azure AI Language resource must be physically located in the specific Azure region where the clinical notes and extracted medical entities are permitted to reside. Creating the resource in the required Azure region ensures that all data at rest and in transit stays within that geographic boundary, which is a fundamental compliance step for HIPAA and data residency. Other configurations like encryption keys or private endpoints enhance security but do not satisfy the core residency requirement.

Answer

Enable diagnostic logging for all operations.

Answer

Use a customer-managed key (CMK) for encryption.

Answer

Enable private endpoint for the AI resource.

Question 7

You are deploying a chatbot using Azure AI Bot Service and Language Understanding (LUIS). The bot must understand user intent from free-text input. Which component should you train?

Accepted Answer

Language Understanding (LUIS) model. The Language Understanding (LUIS) model is the correct component to train because the bot needs to interpret free-text user input and extract intent. LUIS is a natural language processing service specifically designed for intent recognition and entity extraction from conversational phrases. Training the LUIS model with labeled utterances teaches it to map user expressions to predefined intents, enabling the bot to understand and respond appropriately.

Answer

Speech-to-text model

Answer

QnA Maker knowledge base

Answer

Computer Vision model

Question 8

Which TWO actions should you take to ensure high availability for an Azure AI service deployed in a single region?

Accepted Answer

Configure autoscaling for the AI service.. Option A is correct because autoscaling allows the Azure AI service to dynamically adjust the number of instances based on demand, ensuring that the service can handle traffic spikes without downtime. This is a key high-availability feature within a single region, as it prevents resource exhaustion and maintains responsiveness. Option C is correct because deploying the AI service in a second Azure region provides geographic redundancy, enabling failover if the primary region experiences an outage, which is a fundamental high-availability strategy.

Answer

Use a read-only replica of the AI service.

Answer

Enable Azure DDoS Protection.

Answer

Enable Azure Backup for the AI resource.

Question 9

Which THREE factors should you consider when selecting an Azure AI service for a solution that processes multilingual content?

Accepted Answer

Availability of custom translation models.. Option A is correct because Azure AI Translator and Azure AI Language support custom translation models via the Custom Translator feature, which allows you to build and deploy domain-specific translation models for multilingual content. This is critical when generic translation models fail to handle industry-specific terminology or brand voice, ensuring higher accuracy for specialized use cases like legal or medical documents.

Answer

Geographic region of the resource.

Answer

Pricing per transaction.

Question 10

Which TWO Azure services are used together to build a custom question-answering solution?

Accepted Answer

Azure AI Language (Custom Question Answering). Azure AI Language's Custom Question Answering (formerly QnA Maker) provides the core service for building a knowledge base of question-answer pairs and processing user queries. Azure AI Search acts as the underlying search index and retrieval engine, enabling the solution to scale, perform semantic ranking, and return relevant answers from the knowledge base. Together, they form a complete custom Q&A pipeline: Custom Question Answering for authoring and orchestration, and Azure AI Search for indexing and retrieval.

Answer

Azure AI Computer Vision

Answer

Azure AI Speech

Answer

Azure AI Translator

Question 11

Your organization deploys an Azure AI Foundry solution for a customer service chatbot. The chatbot uses a large language model (LLM) hosted on Azure OpenAI Service with a GPT-4 model. Requirements: (1) The chatbot must only use information from the company's internal knowledge base, not general internet knowledge. (2) Responses must include citations from the knowledge base. (3) The solution must filter out any toxic or harmful content. (4) The chatbot must be deployed in a secure environment with network isolation. You have an Azure AI Foundry project with a connected Azure OpenAI resource. The knowledge base is stored in Azure AI Search. You need to configure the solution. What should you do?

Accepted Answer

Use Azure OpenAI on your data with Azure AI Search as the data source, enable content filtering, and configure the model to use the search index with strict grounding.. Option C is correct because it uses Azure OpenAI on your data with Azure AI Search as the data source, which ensures the model only retrieves and generates responses from the indexed knowledge base, meeting the requirement to avoid general internet knowledge. Enabling content filtering satisfies the toxicity requirement, and configuring strict grounding ensures responses include citations from the search index. The secure environment with network isolation is achieved through Azure AI Foundry's managed network capabilities, which are compatible with this configuration.

Answer

Use Azure OpenAI with function calling to retrieve knowledge base documents, and enable content filtering.

Answer

Use prompt engineering with system messages to restrict knowledge, and enable content filtering.

Answer

Fine-tune the GPT-4 model on the knowledge base and deploy with content filtering.

Question 12

You are designing an Azure AI solution for a global e-commerce company. The solution must: (1) Translate product descriptions into 12 languages in real-time. (2) Detect sentiment in customer reviews for each language. (3) Extract key product attributes (e.g., color, size) from unstructured review text. (4) Store results in a centralized database for analytics. The solution must minimize latency and cost. You plan to use Azure AI services. Which combination of services should you use?

Accepted Answer

Azure AI Translator for translation, Azure AI Language for sentiment and entity extraction, and Azure AI Search to index the results.. Option D is correct because Azure AI Translator provides real-time translation into 12 languages, Azure AI Language handles both sentiment analysis and entity extraction (for attributes like color and size) from unstructured text, and Azure AI Search indexes the results for low-latency analytics. This combination minimizes latency by using dedicated services for each task and avoids unnecessary overhead from services like Speech or Computer Vision that are not required for text-only processing.

Answer

Azure AI Language for translation and sentiment, and Azure AI Computer Vision for attribute extraction.

Answer

Azure AI Speech for translation, Azure AI Language for sentiment, and Azure AI Search for storage.

Answer

Azure AI Translator for translation, Azure AI QnA Maker for attribute extraction, and Azure SQL Database for storage.

Question 13

A financial services company uses Azure AI Document Intelligence to process loan applications. The solution extracts data from uploaded PDFs and stores it in Azure Cosmos DB. Recently, extraction errors increased due to poor-quality scans. The company needs to improve accuracy without manual intervention. The solution must also handle varying document layouts. You need to recommend a plan. What should you do?

Accepted Answer

Train a custom document extraction model using labeled samples of poor-quality scans and different layouts.. Option D is correct because training a custom document extraction model using labeled samples of poor-quality scans and varying layouts directly addresses the root cause of extraction errors. Azure AI Document Intelligence's custom model capability allows you to train on specific document types and quality issues, improving accuracy without manual intervention. This approach adapts to the company's need to handle diverse layouts and degraded image quality, which prebuilt models cannot reliably manage.

Answer

Increase the throughput (TPS) of the Document Intelligence resource.

Answer

Enable OCR enhancement in Document Intelligence.

Answer

Switch to Azure AI Language for entity extraction.

Question 14

You are designing an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle up to 1000 invoices per hour with a maximum latency of 5 seconds per invoice. You need to choose the appropriate pricing tier and resource configuration. What should you do?

Accepted Answer

Use the S0 tier and configure autoscaling if needed. The S0 tier (Standard) for Azure AI Document Intelligence supports up to 15 transactions per second (TPS), which equates to 54,000 invoices per hour—far exceeding the 1,000 per hour requirement. With a maximum latency of 5 seconds per invoice, the S0 tier's default throughput is sufficient without needing autoscaling, though autoscaling can be configured for burst scenarios. The Free tier (F0) is limited to 20 transactions per minute (1,200 per hour) and is not designed for production workloads, making S0 the correct choice.

Answer

Create a Cognitive Services multi-service account to increase throughput

Answer

Use the Free tier and implement a retry policy

Answer

Deploy the Document Intelligence resource in multiple regions

Question 15

Your company uses Azure AI Language to analyze customer feedback. The solution currently uses the default endpoint and key. Security policy requires that all API calls be authenticated using Microsoft Entra ID and that network access be restricted to a specific virtual network. You need to reconfigure the resource. What should you do?

Accepted Answer

Disable local authentication, enable managed identity, and configure the virtual network. Option A is correct because it addresses both security requirements: disabling local authentication ensures that API keys cannot be used, forcing all calls to authenticate via Microsoft Entra ID; enabling a managed identity provides a secure identity for the resource to authenticate with Entra ID; and configuring the virtual network restricts network access to the specified VNet, meeting the network restriction policy.

Answer

Regenerate the API keys and update the application

Answer

Create a private endpoint for the resource

Answer

Configure the resource to use Microsoft Entra ID authentication only

AI-102 Plan and manage an Azure AI solution Practice Test 13 — 15 Questions

Exhibit

AI-102 Plan and manage an Azure AI solution Practice Test 13 — 15 Questions

Exhibit