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

Question 1

A team is using Azure OpenAI Service to summarize long legal contracts. They observe that summaries sometimes miss clauses located near the end of the document. The contracts are far longer than the model's maximum context window. What should they implement to improve coverage of the entire document?

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

Accepted Answer

Split the contract into overlapping chunks, summarize each chunk, then combine the partial summaries into a final summary.. Documents longer than the context window must be processed in pieces. Overlapping chunking ensures clauses spanning boundaries survive, and a combine step merges per-chunk summaries into a coherent whole. Sampling parameters and output length settings operate on generation behavior and cannot extend how much source text the model receives.

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Lower the max_tokens parameter to force the model to read further into the document.

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Increase the temperature parameter so the model explores more of the contract content.

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Set the top_p parameter to 1.0 and rely on nucleus sampling to expand context coverage.

Question 2

A developer is building a generative AI application with Azure OpenAI Service. The application must stream partial responses to users as tokens are generated, rather than waiting for the complete response. Which parameter should the developer set in the chat completion request?

Accepted Answer

Set 'stream' to true.. Streaming is controlled by the stream parameter, which when true causes the service to emit incremental delta chunks over a persistent connection. This lets the application display tokens as they are generated, reducing perceived latency while keeping the same model and prompt configuration.

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Set 'temperature' to 0.

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Set 'max_tokens' to a large value.

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Set 'n' to a value greater than 1.

Question 3

You are developing a generative AI feature with Azure OpenAI Service. The feature must generate a structured JSON object that conforms to a specific schema so it can be consumed by a downstream application without post-processing. What should you use to constrain the model output to the schema?

Accepted Answer

Structured outputs with a JSON schema supplied in the request.. Structured outputs in Azure OpenAI allow a JSON schema to be provided with the request so the model's response is constrained to conform to that schema. This guarantees valid, parseable JSON with the expected fields, eliminating brittle post-processing and making the output directly consumable by the downstream application.

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A higher frequency_penalty to discourage extra tokens.

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Setting n to a value greater than 1 to generate multiple candidate responses.

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A system message that politely asks the model to return JSON.

Question 4

You are deploying a generative AI chat application that calls an Azure OpenAI model deployment. The application must stream partial completions to the browser as tokens are produced and must reduce perceived latency for long answers. Which two request options should you configure? (Choose two.)

Accepted Answer

Set stream to true on the chat completions request. Streaming has two cooperating parts: the request must set stream to true so the service emits incremental deltas, and the client must read those server-sent events and forward them to the browser. Together they let users see text as it is generated. Parameters that alter sampling, penalties, or probabilities change content or add metadata but never change delivery timing.

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Increase the frequency_penalty value to discourage repeated tokens

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Set the logprobs parameter to return token-level probabilities

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Set n to a value greater than one to generate multiple candidate completions

Question 5

You are developing a generative AI solution using Azure OpenAI Service. The solution must generate product descriptions based on a set of attributes provided by the user. You need to ensure the output adheres to a specific JSON schema. Which feature should you use to enforce the structure of the model's response?

Accepted Answer

Structured outputs with a JSON schema definition.. Structured outputs in Azure OpenAI Service enable you to specify a JSON schema that the model's response must conform to. This guarantees that the generated output is valid JSON and includes all required fields, which is essential for generating product descriptions with a consistent format. It is a robust way to enforce structure without relying on prompt engineering alone.

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Function calling with a defined function that returns JSON.

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Temperature set to 0 to make output deterministic.

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Prompt engineering with few-shot examples of JSON.

Question 6

You are building an internal assistant on Azure OpenAI that must stream responses to a React web app while hiding the model endpoint key from the browser. The app already authenticates users with Microsoft Entra ID. You need the least-privilege approach that keeps the key out of client code. What should you do?

Accepted Answer

Create an Azure API Management instance that fronts Azure OpenAI, and have the React app call API Management with the user's Entra ID token.. The key must never reach the browser, so a server-side intermediary is required. Azure API Management can authenticate the Entra ID token, enforce policies, and inject the Azure OpenAI key from a secure store while preserving streaming responses. Managed identities cannot run in a browser, and retrieving a key client-side exposes it regardless of where it was stored.

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Give the React app a system-assigned managed identity and call Azure OpenAI directly from the browser.

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Store the Azure OpenAI key in Azure Key Vault and have the React app retrieve it at runtime using the signed-in user's token.

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Embed the Azure OpenAI key in the React build and call the completions endpoint directly from the browser.

Question 7

You are building a generative AI application with Azure OpenAI Service that summarizes long legal contracts. Legal reviewers report that the summaries omit obligations buried in the middle of very long documents, even though the documents fit within the model's maximum context window. You need to improve recall of those mid-document clauses while minimizing added latency. What should you do first?

Accepted Answer

Split the contract into overlapping chunks, summarize each chunk, then summarize the combined chunk summaries.. Long inputs suffer from uneven attention, so obligations in the middle can be missed even when they fit the context window. Chunking with overlap and summarizing hierarchically places every clause in a short, high-attention context and preserves clauses that cross boundaries. Raising temperature, enlarging the window, or adding instructions does not change how attention is distributed, so those approaches leave the recall gap unresolved.

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Add a system message instructing the model to read the entire document carefully before summarizing.

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Increase the model's temperature setting so the model explores more of the document content.

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Switch the deployment to a model with a larger maximum context window and resend the full contract.

Question 8

You are developing a generative AI application using Azure OpenAI. The application must log all prompts and completions for auditing, but sensitive data such as personal health information (PHI) must be redacted before logging. Which approach should you use?

Accepted Answer

Use Azure AI Language's PII detection to identify and redact PHI in prompts and completions before writing to your logging store.. To audit prompts and completions while protecting PHI, integrate Azure AI Language PII detection into your logging pipeline. Redact sensitive entities before persisting logs. This satisfies auditing requirements and compliance mandates, whereas relying on diagnostic logs or storage encryption alone would leave PHI exposed.

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Enable Azure OpenAI diagnostic settings to send logs to Azure Monitor, and rely on built-in PHI redaction.

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Configure Azure OpenAI to not log prompts and completions, and instead log only token counts and model names.

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Store logs in an Azure Storage account with encryption at rest and enable soft delete.

Question 9

You are building a generative AI feature in an application using the Azure OpenAI SDK. The feature must stream tokens to the user as they are generated and must also capture the full response for logging. Which implementation approach satisfies both requirements?

Accepted Answer

Set stream=true on the chat completions request, iterate over the streamed chunks to display deltas, and accumulate the delta content to form the complete response for logging.. Streaming chat completions deliver delta content across multiple chunk objects. Rendering those deltas satisfies the progressive display requirement, and concatenating them rebuilds the exact full message for logging. A single streaming request therefore meets both needs, whereas duplicate calls, partial logging, or multiple completions fail one requirement or the other.

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Disable streaming and set n=2 so the model returns two completions, displaying one and logging the other.

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Make two separate non-streaming requests: one to display the response and one to log it.

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Set stream=true and log only the first chunk received from the stream.

Question 10

Your team is building an internal knowledge assistant with Azure OpenAI Service. Legal requires that every generated answer include a traceable reference to the source document so reviewers can verify claims. The documents already live in Azure AI Search, and you want the service to return citation metadata alongside the generated text. Which feature should you configure?

Accepted Answer

The On Your Data feature configured with an Azure AI Search data source, which returns citations with the response.. On Your Data integrates an Azure AI Search index as the grounding source and returns the model's answer together with citation information identifying the retrieved documents. Because the documents already live in Azure AI Search and the requirement is traceable references in the output, this feature directly provides the needed attribution without custom retrieval code.

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Azure OpenAI content filtering with annotating models enabled on the deployment.

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A system message instructing the model to always mention the file name it used to answer.

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Logging every request and response to Azure Monitor and querying the logs after the fact.

Question 11

A marketing team uses Azure OpenAI Service to draft product descriptions. They report that outputs vary widely in tone and sometimes ignore the required brand voice. You need to make responses follow a fixed set of style rules consistently across all requests without changing the model deployment. What should you do?

Accepted Answer

Add a system message that defines the brand voice and formatting rules. System messages carry instructions the model treats with high priority and apply to the whole request, so they are the right mechanism for enforcing brand voice, tone, and formatting rules. Sampling parameters such as temperature and presence_penalty alter randomness and repetition, and additional deployments of the same model behave identically, so none of them can encode style policy.

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Create a second deployment of the same model with a different name

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Raise the temperature value to increase output creativity

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Set the presence_penalty parameter to a large positive value

Question 12

You are deploying a generative AI application using Azure OpenAI Service. The application must generate responses that are creative and varied for a marketing campaign. Which parameter should you adjust to increase the randomness of the model's output?

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of the model's output. Higher temperatures lead to more diverse and creative responses, which is suitable for a marketing campaign that requires varied content. Other parameters like top-p and penalties affect diversity in different ways but are not the primary control for randomness.

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Presence penalty

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Top-p (nucleus sampling)

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Frequency penalty

Question 13

A legal team wants an assistant that drafts contract summaries. Their policy requires that every generated summary include traceable references to the exact clauses used and that reviewers be able to see which source passages informed each statement. You are using Azure OpenAI with your own document index. Which approach best meets the traceability requirement?

Accepted Answer

Use the On Your Data pattern with Azure AI Search, return document chunks with their IDs and titles, and instruct the model to cite the retrieved chunk identifiers in its output.. Traceability requires linking generated text back to specific source passages. Using Azure AI Search as the grounding source with the On Your Data pattern returns citation metadata for the retrieved chunks, and prompting the model to cite those chunks produces summaries where each statement can be traced to a clause. Quota, fine-tuning, and logprobs do not create that link.

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Fine-tune the base model on previously approved contract summaries so its style matches legal expectations.

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Enable the model's logprobs parameter and expose the token probabilities to reviewers as evidence of reliability.

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Increase the model deployment's tokens-per-minute quota so longer contracts fit in a single prompt.

Question 14

You are building an internal knowledge assistant with Azure OpenAI Service. Responses must be grounded in a curated set of HR policy documents stored in Azure AI Search, and every response must include a citation to the specific source chunk. You already deployed a GPT-4o model and created the search index. You need to add the grounding layer with the least development effort. What should you do?

Accepted Answer

Enable the "On Your Data" feature on the model deployment and point it at the Azure AI Search index.. Grounding a deployment in a private index with automatic citations is exactly what the On Your Data capability provides for Azure OpenAI. Since the Azure AI Search index is already built, enabling this feature on the deployment satisfies the grounding and citation requirements with configuration instead of custom code, which matches the least-effort constraint.

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Increase the model temperature and max tokens so the model has more room to recall the HR policies from its pretrained knowledge.

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Call the Azure AI Search REST API from the client, concatenate the top results into the system message, and post-process the model output to append source links.

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Fine-tune the GPT-4o deployment on the HR policy documents, then rely on the model to quote the correct policy section.

Question 15

You are building a generative AI assistant with Azure OpenAI Service that must answer questions using a large corpus of internal product manuals. The manuals are updated weekly, and the assistant must reflect changes without retraining the model. You need to implement a retrieval-augmented generation (RAG) pattern. What should you use to index and retrieve relevant manual content?

Accepted Answer

Create an Azure AI Search index with vector embeddings of the manual chunks and use it as a data source in the Azure OpenAI 'on your data' configuration.. The correct approach is to use Azure AI Search with vector embeddings as a data source for Azure OpenAI 'on your data'. This enables retrieval-augmented generation, where relevant manual chunks are retrieved and supplied to the model, ensuring responses reflect weekly updates without retraining. It is the supported, scalable method for grounding generative AI in dynamic internal documents.

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Use Azure Cosmos DB with the built-in vector search and connect it directly to the model via a function calling tool.

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Store the manuals as plain text in Azure Blob Storage and pass the entire corpus in each prompt.

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Fine-tune the base model on the manuals each week using Azure OpenAI fine-tuning.