AI-102 Implement generative AI solutions Practice Test 6 — 15 Questions

Question 1

You are a machine learning engineer at a large retail company. The company has thousands of product descriptions that need to be updated regularly. They currently use a manual process. You propose using Azure OpenAI to generate new descriptions based on product attributes. You have a dataset of existing product descriptions and attributes stored in an Azure SQL Database. The solution must be cost-effective, scalable, and must not require retraining the model. You need to design the solution. You have the following options:

Option A: Use Azure OpenAI with few-shot learning by including examples in the prompt for each product. Deploy the model on an Azure Kubernetes Service (AKS) cluster for high throughput.

Option B: Use Azure OpenAI with prompt templates that include product attributes and call the API for each product. Use Azure Logic Apps to orchestrate the workflow and store results back to Azure SQL Database.

Option C: Fine-tune a custom model on the existing product descriptions and deploy it as a managed endpoint. Use Azure Data Factory to batch process all products.

Option D: Use Azure OpenAI with the batch API to generate descriptions for all products at once, using a single prompt that lists all products and attributes. Store the batch output in Azure Blob Storage and then import into Azure SQL Database.

Which option should you choose?

Accepted Answer

Option B. (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.

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Option C

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Option D

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Option A

Question 2

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 3

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it provides built-in content moderation APIs that detect and filter harmful or offensive text and images, including hate speech, violence, self-harm, and sexual content. Integrating this service with the Azure OpenAI chatbot ensures that user inputs and model outputs are screened in real time, preventing the generation of inappropriate responses.

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Azure Bot Service

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Azure Cognitive Search

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Azure Form Recognizer

Question 4

A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.

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Increase the top_p parameter

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Increase the max_tokens parameter

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Increase the frequency_penalty parameter

Question 5

A developer wants to integrate a pre-built AI model that can extract key information from invoices, such as vendor name, invoice date, and total amount. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents like invoices, including fields such as vendor name, invoice date, and total amount. It uses pre-built models trained on common document types, making it ideal for this use case without requiring custom training.

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Azure AI Language

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Azure OpenAI Service

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Azure Cognitive Search

Question 6

An organization is deploying a conversational AI solution using Azure OpenAI. They want to ensure the model's responses are grounded in their own knowledge base documents to reduce hallucinations. Which approach should they implement?

Accepted Answer

Integrate Azure Cognitive Search for retrieval-augmented generation (RAG). Retrieval-Augmented Generation (RAG) with Azure Cognitive Search allows the model to dynamically retrieve relevant chunks from the organization's knowledge base documents at inference time. This grounds responses in authoritative, up-to-date content, directly reducing hallucinations by providing factual context rather than relying solely on the model's parametric memory.

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Fine-tune the model on the knowledge base documents

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Implement Azure AI Content Safety filters

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Use prompt engineering to instruct the model to only use the knowledge base

Question 7

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

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Use Azure AI Document Intelligence to extract text from PDFs before indexing

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Deploy Azure AI Content Safety to filter responses

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Fine-tune the GPT model on the PDF content

Question 8

You are a data scientist at a healthcare company. You have deployed a GPT-4 model using Azure OpenAI to answer patient inquiries about medical conditions. The model is configured with temperature=0.3 and max_tokens=200. Recently, the compliance team flagged that some responses contain contradictory information compared to the official medical guidelines. You need to ensure the model's answers align strictly with the provided medical documents (stored as PDFs in Azure Blob Storage). You have access to Azure Cognitive Search and Azure AI Document Intelligence. The solution must minimize hallucinations and not require retraining the model. What should you do?

Accepted Answer

Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.. It uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.

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Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.

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Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.

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Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.

Question 9

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 10

A company uses Azure OpenAI Service to generate product descriptions. They notice that the descriptions sometimes contain factually incorrect information. Which strategy should they use to reduce hallucinations?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) by grounding prompts with a knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base, providing factual context that directly reduces hallucinations. By retrieving relevant documents and injecting them into the prompt, the model generates responses based on verified information rather than relying solely on its parametric memory, which is the primary cause of factual inaccuracies in Azure OpenAI Service.

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Increase the temperature parameter to 1.0.

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Reduce the max_tokens parameter to limit output length.

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Add a system message instructing the model to be more careful.

Question 11

You are designing a generative AI solution that uses Azure OpenAI GPT-4 to answer customer support questions. The solution must comply with Microsoft's Responsible AI principles, particularly transparency and accountability. Which implementation approach best meets these requirements?

Accepted Answer

Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.. It directly addresses Microsoft's Responsible AI principles of transparency and accountability. Enabling content filtering (via Azure AI Content Safety) ensures harmful outputs are blocked, logging all interactions provides an audit trail for accountability, and including a disclaimer that responses are AI-generated satisfies transparency by clearly informing users they are interacting with an AI system.

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Use the model without any modifications, and have a human review all responses.

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Fine-tune the model on a curated dataset of support tickets and disable content filtering.

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Use the default model deployment and rely on the model's inherent safety.

Question 12

You need to generate realistic synthetic data for training a machine learning model while ensuring the data does not contain personally identifiable information (PII). Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models (e.g., GPT-4) that can create realistic synthetic data by learning patterns from training data. Crucially, these models can be configured to avoid memorizing or reproducing PII, and you can apply content filters and data masking to ensure the generated output is free of personally identifiable information.

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Azure AI Search

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Azure AI Document Intelligence

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Azure AI Language

Question 13

A company uses Microsoft Copilot for Microsoft 365 to automate email responses. They want to ensure that the Copilot responses comply with their data governance policies and do not expose sensitive information. What should they configure?

Accepted Answer

Microsoft Purview Data Loss Prevention (DLP) policies. Microsoft Purview Data Loss Prevention (DLP) policies are designed to detect and prevent the accidental sharing of sensitive information, such as credit card numbers or personally identifiable information (PII), across Microsoft 365 services. By configuring DLP policies, the company can scan Copilot-generated email responses for sensitive data patterns and block or warn before the email is sent, ensuring compliance with data governance policies.

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Microsoft Entra ID conditional access policies

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Microsoft Intune app protection policies

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Microsoft Sentinel analytics rules

Question 14

You are building a generative AI application that must process large volumes of PDF documents and generate summaries using Azure OpenAI. The solution must be cost-effective and handle variable workloads. Which architecture should you recommend?

Accepted Answer

Use Azure Functions with a consumption plan to trigger processing jobs and call Azure OpenAI.. Azure Functions with a consumption plan provides a serverless, event-driven architecture that scales automatically to handle variable workloads, ensuring cost-effectiveness by charging only for compute time used. This architecture is ideal for processing large volumes of PDFs, as each document can trigger a function execution that calls Azure OpenAI for summarization, without the need for always-on infrastructure.

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Use Azure Kubernetes Service (AKS) with a persistent node pool of GPU nodes.

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Deploy a GPU-enabled virtual machine and run the summarization jobs sequentially.

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Use Azure Logic Apps to iterate through documents and call Azure OpenAI.

Question 15

You need to generate a poem using Azure OpenAI. The poem should be about nature and have a cheerful tone. Which parameter should you adjust to influence the tone?

Accepted Answer

system message. The system message (D) is the correct parameter to influence the tone of a generated poem because it acts as a high-level instruction that sets the behavior, persona, and style of the model. By including a directive like 'You are a cheerful poet writing about nature,' you directly control the tone without altering randomness or output length.

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top_p

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temperature

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max_tokens