AI-102 Implement generative AI solutions Practice Test 14 — 15 Questions

Question 1

You are building an Azure OpenAI solution that must generate product descriptions from a small set of 40 curated examples that demonstrate your brand voice. You want the model to imitate the style without changing the model weights or incurring the cost of a fine-tuning job. What should you do?

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

Accepted Answer

Include several of the curated examples directly in the system and user messages of each chat completion request.. Few-shot prompting embeds a handful of curated examples in the prompt so the model mimics their tone, structure, and vocabulary on each call. Because the requirement is a small example set and no weight modification or fine-tuning cost, in-context examples are the correct mechanism. Sampling parameters and retrieval indexes do not substitute for style conditioning.

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Set the temperature parameter to 2 and the top_p parameter to 0.1 on every request.

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Upload the examples to an Azure AI Search index and enable semantic ranker on the index.

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Create a fine-tuning job in Azure OpenAI Studio using the 40 examples and deploy the resulting custom model.

Question 2

You are building a generative AI solution with Azure OpenAI Service. Prompts must be assembled from a system instruction, retrieved document chunks, and the user's question. You need a mechanism that automatically inserts the retrieved chunks into a designated placeholder in a prompt template before the request is sent to the model. What should you use?

Accepted Answer

Prompt flow with a Jinja prompt template node. Prompt flow's Jinja prompt template node is the supported way to author a reusable template whose placeholders are filled from flow inputs at runtime, which is exactly what injecting retrieved chunks requires. The other choices address safety filtering, authentication, or output length, none of which assemble prompt text. Using a template also keeps prompt logic versioned and testable within the flow.

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A content filter configured on the Azure OpenAI deployment

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A system-assigned managed identity on the Azure OpenAI resource

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The max_tokens parameter on the chat completions request

Question 3

You are building a generative AI solution that uses Azure OpenAI function calling to let the model invoke backend APIs. During testing, the model sometimes invents parameter values that cause API errors. You need to make function invocation more reliable. What should you do?

Accepted Answer

Define clear function schemas with typed parameters, required fields, and descriptions, and validate arguments before executing the API call.. Reliable function calling depends on well-defined schemas and validation. Typed parameters, required fields, and descriptive names help the model map user intent to correct arguments, and validating before execution prevents invalid values from reaching the API. Higher temperature, oversized specifications, and parallel-call limits do not enforce argument correctness.

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Set tool_choice to auto and increase the temperature to encourage more creative argument generation.

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Disable parallel tool calls and force the model to call only one function per turn.

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Include the full OpenAPI specification of every backend API in the system message.

Question 4

You are building a generative AI solution with Azure OpenAI Service that must produce concise, factual answers using only a supplied knowledge base. During testing, the model sometimes invents details not present in the knowledge base. You need to reduce hallucinations without retraining the model. What should you do?

Accepted Answer

Lower the temperature and include a system message that instructs the model to answer only from the provided context and to say it does not know when the answer is absent.. Hallucinations in grounded generation are commonly reduced with prompt engineering: lower temperature to make output more deterministic, and a system message that restricts answers to the provided context and instructs the model to admit when the answer is not present. These changes need no retraining and can be applied immediately to the deployment.

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Increase the max_tokens setting so the model has more room to explain its reasoning.

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Switch to a larger model deployment with a bigger context window.

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Fine-tune the base model on the knowledge base so it memorizes the facts.

Question 5

You are deploying a generative AI solution that uses Azure OpenAI Service with your own data stored in Azure AI Search. Users report that answers are sometimes pulled from documents the user is not permitted to see, because the retrieval step searches the entire index. You need to ensure each user only receives answers grounded in documents they are authorized to access, without creating a separate index per user. What should you do?

Accepted Answer

Add a security filter field to the Azure AI Search index and pass the user's group or tenant identifier as an OData filter on each query.. Authorization must be enforced where the documents are selected, not where the model generates text. Adding a filterable access-control field to the Azure AI Search index and applying the caller's identity as an OData filter causes the retrieval step to return only permitted chunks, so the model can never ground an answer in a restricted document. This preserves one shared index while honoring per-user permissions.

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Enable Azure OpenAI content filtering at High severity for both prompts and completions.

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Create a separate Azure OpenAI deployment for each department and route users to the deployment matching their department.

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Increase the chunk size of the indexed documents so that fewer documents are returned per query.

Question 6

You are building a generative AI assistant that must summarize long technical manuals stored in Azure Blob Storage. The manuals are often 300 pages, and the model must produce a concise summary with references to page numbers. Which approach should you use?

Accepted Answer

Use Azure AI Search to index the manuals with page metadata, then use a retrieval-augmented generation (RAG) pattern with Azure OpenAI to generate summaries and citations.. Long documents exceed model context windows, so retrieval-augmented generation is needed. Azure AI Search indexes content with metadata, allowing Azure OpenAI to generate grounded summaries and cite page numbers. This combination handles length, improves accuracy, and provides traceability, which is essential for technical manuals.

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Use Azure AI Document Intelligence to extract text, then send each page as a separate request to Azure OpenAI and concatenate the summaries.

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Use Azure OpenAI fine-tuning to train a custom model on the manuals, then ask the model to summarize any manual.

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Use Azure OpenAI GPT-4o with a single prompt containing the entire manual text.

Question 7

You are deploying a generative AI assistant on Azure OpenAI Service that must summarize user-supplied financial reports. Security policy requires that report contents never leave your Azure tenant and that the model must not be fine-tuned. You need to provision the resource so that prompt and completion data are not retained for human review and are not used to train any shared model. What should you configure?

Accepted Answer

Submit an Azure OpenAI limited access modification request to disable abuse monitoring for the subscription.. The requirement is about data handling by the service itself, not about access control or content safety. The only mechanism that removes default human review and retention of prompts and completions for an Azure OpenAI resource is an approved limited access modification for abuse monitoring on the subscription. Content filters, diagnostic settings, and RBAC all address different concerns and leave the retention behavior unchanged.

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Set the Azure OpenAI resource's 'Content logging' to Disabled in Azure Monitor diagnostic settings.

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Create the deployment in a separate resource group and assign the Cognitive Services User role only to the application's managed identity.

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Deploy the model with a content filter policy set to block high-severity categories and enable prompt shields.

Question 8

You are deploying an Azure OpenAI model for a public-facing FAQ assistant. The assistant must answer only questions covered by a fixed set of approved topics, and any off-topic question should receive a polite refusal. Which approach most directly enforces this behavior?

Accepted Answer

Configure a system message that defines the allowed topics and instructs the model to refuse anything outside them.. The system message is the model's persistent instruction set, making it the natural place to declare allowed topics and refusal behavior. For a fixed FAQ scope, this directly shapes every response. The other settings influence sampling, safety categories, or capacity, none of which restrict the assistant to approved topics.

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Raise the frequency_penalty parameter to reduce repeated off-topic phrases.

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Deploy the model with the lowest available quota tier to limit how many questions users can ask.

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Set the model deployment's content filter to block the 'violence' category at high severity.

Question 9

You are deploying a generative AI solution by using Azure OpenAI Service. The solution must ground model responses in a private Azure AI Search index that contains product manuals. You need to configure the model deployment so that the service automatically retrieves relevant chunks from the index and includes them in the prompt at inference time. Which configuration should you use?

Accepted Answer

Enable the 'Azure OpenAI On Your Data' feature by specifying the Azure AI Search data source in the model's chat completion request.. Azure OpenAI On Your Data is designed to connect a deployment to an Azure AI Search index so that retrieval and prompt augmentation happen automatically at request time. It handles embedding, chunk ranking, and citation generation, which matches the requirement to ground responses in private manuals without custom orchestration.

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Upload the manuals to the model's training dataset and enable continuous training on the deployment.

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Fine-tune the base model on the contents of the product manuals and deploy the fine-tuned model for inference.

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Create a custom retrieval pipeline by using Azure Functions that queries the index and appends results to each user message before calling the model.

Question 10

You deploy a GPT-4o model in Azure OpenAI Service for an internal assistant that summarizes long contracts. Legal requires that every generated summary be traceable to the exact source passages and that the assistant never answer from general world knowledge. You need the model to ground each statement in retrieved text and expose the supporting passages to the caller. What should you configure?

Accepted Answer

Use the Azure OpenAI On Your Own Data feature with citations enabled. Grounding the assistant in an indexed corpus and returning citations is what makes each summary statement traceable to a specific contract passage. Azure OpenAI On Your Own Data performs retrieval against Azure AI Search, injects the retrieved chunks, and returns citation metadata with the response. Sampling parameters, output length, and monitoring logs change behavior or observability but never bind statements to sources.

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Enable the abuse monitoring logging option on the Azure OpenAI resource

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Increase the model's max_tokens value to fit the entire contract

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Set temperature to 0 and top_p to 1 on the chat completions call

Question 11

You are implementing a generative AI solution using Azure OpenAI Service. The solution must generate responses that are grounded in your organization's proprietary documents and must return citations that link back to the source documents. You need to configure the deployment to meet these requirements. Which two actions should you perform? (Choose two.)

Accepted Answer

Create an Azure AI Search index that contains the proprietary documents with retrievable content and citation fields such as title and URL.. Grounded responses with citations in Azure OpenAI require a retrieval source and a deployment-level data source binding. An Azure AI Search index holds the proprietary documents with content and citation metadata, and associating that index with the model deployment through On Your Data enables service-side retrieval, prompt injection, and citation generation.

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Enable diagnostic logging on the Azure OpenAI resource to capture prompt and completion text.

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Fine-tune the model on the proprietary documents so it can reproduce them verbatim.

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Deploy a second model instance in a different Azure region for high availability.

Question 12

You are building a generative AI application with Azure OpenAI Service. The application must generate responses that are grounded in a specific set of internal documents stored in an Azure AI Search index. You want to use the built-in 'On Your Data' feature. Which configuration parameter should you set to ensure the model retrieves relevant documents before generating a response?

Accepted Answer

Set the data source type to Azure Cognitive Search and provide the index name.. The On Your Data feature in Azure OpenAI Service allows the model to retrieve relevant documents from a specified data source, such as Azure Cognitive Search, before generating a response. By setting the data source type to Azure Cognitive Search and providing the index name, the model can ground its answers in the internal documents. This is the intended method for retrieval-augmented generation with Azure OpenAI.

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Enable the 'temperature' parameter to a low value to make responses more factual.

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Set the model deployment to a fine-tuned model trained on the documents.

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Use the 'max_tokens' parameter to limit the response length to match document snippets.

Question 13

You are deploying a retrieval-augmented generation chat solution on Azure OpenAI. During testing, users report that the model confidently answers questions using facts that are not in the indexed documents, and that retrieved chunks sometimes come from the wrong department's SharePoint site. You need to reduce these ungrounded answers without retraining the model. (Choose two.)

Accepted Answer

Set the message role "system" content to instruct the model to answer only from the provided context and to state when the answer is not found.. Ungrounded answers in a RAG pattern usually come from two sources: the model not being constrained to the retrieved context, and the retrieval step returning irrelevant or unauthorized chunks. A system message that enforces answer-only-from-context behavior, combined with metadata filtering and security trimming at the search layer, addresses both causes without retraining. Length and randomness parameters, plus harmful-content filters, do not influence factual grounding.

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Set the temperature parameter to 2 to make the model more factual.

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Increase the max_tokens parameter so the model has more room to explain its reasoning before answering.

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Enable content filtering at the highest severity threshold to block ungrounded statements.

Question 14

You are building a generative AI assistant on Azure OpenAI Service that must invoke backend business functions, such as checking order status and issuing refunds, in response to natural-language requests. You want the model to decide when a function is needed and to supply structured arguments, while your application retains control over execution. Which two actions should you take? (Choose two.)

Accepted Answer

Define the available functions and their parameters in the tools parameter of the chat completions request.. Function calling works in two phases: you declare callable operations and their parameter schemas in the request, and the model returns a structured call when a function is appropriate. Your application then executes that function and returns the output so the model can finish the response. Both declaring the tools and executing plus returning results are required for a working implementation.

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Fine-tune the base model on historical order and refund conversations so it learns to call the correct endpoints.

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Grant the Azure OpenAI resource a managed identity with permission to call the backend APIs directly on behalf of the model.

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Set the temperature parameter to 0 and increase the max_tokens value to guarantee deterministic function selection.

Question 15

You are building a generative AI solution using Azure OpenAI. The solution must generate responses that include factual information from a set of internal documents that are updated frequently. You need to ensure the model uses the latest document versions without retraining. What should you do?

Accepted Answer

Use Azure OpenAI with a retrieval-augmented generation pattern that queries an up-to-date search index of the documents.. Retrieval-augmented generation with a search index allows the model to access current document content at inference time. When documents change, you update the index, and the model retrieves the latest passages. This avoids fine-tuning and ensures responses reflect the most recent information, which is critical for frequently updated knowledge bases.

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Fine-tune the Azure OpenAI model each time the documents are updated.

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Increase the model's temperature setting to encourage more creative and up-to-date responses.

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Embed the documents directly into the model's prompt by concatenating all of them in each request.