AI-102 Timed Practice Test 2 — 10 Questions

Question 1

You deploy the ARM template shown in the exhibit. After deployment, you need to allow access to the Language service from your on-premises application. What should you do?

Accepted Answer

Add an IP rule with your on-premises public IP address.. The ARM template deploys an Azure Cognitive Services Language service with a network ACL that defaults to denying all traffic (defaultAction: Deny). To allow your on-premises application to access the service, you must add an IP rule that permits traffic from your on-premises public IP address. This is because the network ACL evaluates IP rules before the default action, and adding a rule with your public IP overrides the default deny for that specific source.

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Remove the customSubDomainName property.

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Set the defaultAction to Allow.

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Change the SKU to F0 to allow public access.

Question 2

Match each Azure Cognitive Search skill to its capability.

Accepted Answer

Entity Recognition: Identifies and categorizes entities in text as people, places, organizations, etc.. Common Azure Cognitive Search skills include Entity Recognition, Key Phrase Extraction, OCR (for image text extraction), and Sentiment Analysis. The correct matches are A and B. Options C and D are mismatched definitions.

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OCR: Detects language and translates text to a target language.

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Sentiment Analysis: Detects the dominant language of a document.

Question 3

Match each Azure AI scenario to the appropriate service.

Accepted Answer

Building a conversational chatbot: Azure Bot Service. Correct matches: Chatbot -> Bot Service, Image moderation -> Computer Vision, Sentiment -> Text Analytics. Common confusions include associating Bot Service with image tasks or mistaking Cognitive Search for text analytics.

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Building a conversational chatbot: Azure Machine Learning

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Analyzing images for content moderation: Azure Bot Service

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Classifying text by sentiment: Azure Cognitive Search

Question 4

You are a cloud solution architect at a legal firm. The firm needs to automate the summarization of legal documents. They have a large corpus of past case summaries and legal documents stored in Azure Blob Storage. They want to use Azure OpenAI to generate summaries for new documents. The solution must ensure that the generated summaries are accurate and do not contain hallucinated legal facts. The firm also requires that the solution be serverless and minimize operational overhead. You need to design the solution.

Option A: Use Azure OpenAI with a system message that instructs the model to be accurate. Deploy the model as a web app on Azure App Service and call it from Azure Functions triggered by new blob uploads.

Option B: Use Azure OpenAI with Retrieval-Augmented Generation (RAG) by indexing the past case summaries in Azure AI Search. Use Azure Functions to process new documents, retrieve relevant cases, and pass them as context to the model. Store summaries in Azure Cosmos DB.

Option C: Fine-tune an Azure OpenAI model on the past case summaries and deploy it as a managed endpoint. Use Azure Logic Apps to trigger summarization when new blobs are added.

Option D: Use Azure OpenAI with the chat API and provide the entire document in the prompt. Use Azure Container Instances to run a service that calls the API and writes summaries back to Blob Storage.

Which option should you choose?

Accepted Answer

Option B. It uses Retrieval-Augmented Generation (RAG) with Azure AI Search to ground the model's output in verified past case summaries, directly addressing the requirement to avoid hallucinated legal facts. The serverless architecture is achieved via Azure Functions triggered by blob uploads, minimizing operational overhead, while storing summaries in Azure Cosmos DB provides a scalable, low-latency output store.

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Option A

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Option D

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Option C

Question 5

You need to enforce that only users from your Microsoft Entra ID tenant can call your Azure AI Language API endpoint. Which security mechanism should you configure?

Accepted Answer

Microsoft Entra ID authentication. Microsoft Entra ID authentication (formerly Azure AD) allows you to enforce that only users and applications from your specific Entra ID tenant can call the Azure AI Language API. This is achieved by configuring a managed identity or service principal and assigning it the Cognitive Services User role, which ensures that tokens issued by your tenant are required for access. API keys and IP whitelists do not provide tenant-level identity enforcement, and Azure Firewall is a network-level control that does not authenticate individual users or applications.

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API key authentication

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IP whitelist

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Azure Firewall

Question 6

Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?

Accepted Answer

The 'Add your data' feature configured in Azure OpenAI Studio.. The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.

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A fine-tuned custom model.

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An Azure OpenAI embeddings model deployment.

Question 7

You are designing a solution that uses Azure AI Vision to analyze images for moderation. The solution must detect adult content and identify text in images. You need to minimize latency and cost. Which approach should you recommend?

Accepted Answer

Call the Analyze Image API with the 'adult' and 'read' visual features. The Analyze Image API in Azure AI Vision supports multiple visual features in a single call, including 'adult' for adult content detection and 'read' for OCR (text extraction). This minimizes latency by avoiding multiple API calls and reduces cost since you are billed per API call, not per feature.

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Call the Analyze Image API twice: once for adult content and once for OCR

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Use the Computer Vision 3.2 API with the 'adult' and 'OCR' parameters

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Use the Read API for text and the Content Moderator API for adult content

Question 8

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.

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Reduce the maximum number of results returned per query

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Remove all facet fields from the index

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Disable complex query types such as fuzzy and regex

Question 9

You are developing a generative AI application that uses Azure OpenAI Service to summarize large documents. The application experiences high latency when processing requests. You need to reduce the latency without changing the model. What should you do?

Accepted Answer

Reduce the max_tokens parameter in the API request. Reducing the max_tokens parameter limits the length of the generated response, which directly reduces the processing time required by the Azure OpenAI Service to produce the output. Since latency is caused by the model generating a long sequence of tokens, capping the output tokens decreases the number of autoregressive decoding steps, thereby lowering response time without altering the underlying model.

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Increase the temperature parameter

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Increase the top_p parameter

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Increase the max_tokens parameter

Question 10

You are using Azure OpenAI Service to generate product descriptions. You notice that the model occasionally outputs descriptions that contain factual inaccuracies about product specifications. You want to reduce these hallucinations without changing the model. What should you do?

Accepted Answer

Provide the product specifications in the prompt and use the system message to instruct the model to base answers on them.. Providing the product specifications directly in the prompt and using the system message to instruct the model to base its answers on them grounds the generation in factual data, reducing hallucinations. This technique, known as 'grounding' or 'retrieval-augmented generation' (RAG), does not modify the model itself but constrains its output to the provided context, which is the only way to reduce factual inaccuracies without changing model parameters.

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Increase the frequency_penalty parameter.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.