AI-102 Implement generative AI solutions Practice Test 5 — 15 Questions

Question 1

You are deploying a chatbot using Azure OpenAI Service with a custom dataset indexed in Azure AI Search. Users report that the chatbot frequently responds with 'I don't know' for questions that the dataset should cover. What is the most likely cause?

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

Accepted Answer

The confidence threshold in the retrieval configuration is set too low, filtering out relevant chunks.. When the confidence threshold is set too low, Azure AI Search filters out retrieved chunks that don't meet the minimum confidence score, even if those chunks are relevant. This causes the chatbot to respond with 'I don't know' because no sufficiently confident context is passed to the Azure OpenAI model for answer generation. The issue is specifically in the retrieval configuration, not in the model's generation parameters.

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The search scope is limited to a small number of documents.

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The temperature setting in the model deployment is set too high.

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The chunk size in the index is too large, causing irrelevant chunks to be retrieved.

Question 2

Your team is developing an AI-powered document summarization solution using Azure OpenAI. You need to ensure that the solution complies with Microsoft's Responsible AI principles, specifically transparency. Which configuration should you implement?

Accepted Answer

Add a system message that informs users the summary is generated by AI.. Transparency under Microsoft's Responsible AI principles requires that users are aware when they are interacting with an AI system. Adding a system message that explicitly states the summary is AI-generated fulfills this disclosure requirement. Diagnostic logging (A) aids in accountability and debugging but does not directly inform the user. Fine-tuning (B) improves accuracy but does not address transparency. Content filtering (C) mitigates harmful outputs but does not disclose AI involvement.

Answer

Configure diagnostic logging to capture all model inputs and outputs.

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Fine-tune the model on a custom dataset to improve accuracy.

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Enable content filtering with severity levels high and medium.

Question 3

You are building a conversational AI system using Azure OpenAI Service. The system must maintain context across multiple user turns. Which parameter determines how many previous messages are considered for the next response?

Accepted Answer

The length of the messages array in the API call. The Azure OpenAI Service API uses a `messages` array in the request body to represent the conversation history. Each entry in this array corresponds to a previous turn (with roles like 'user', 'assistant', or 'system'), and the length of this array directly determines how many prior messages are considered when generating the next response. By including more messages, you extend the context window; by truncating the array, you limit it.

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max_tokens

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temperature

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top_p

Question 4

You have configured a system message for an Azure OpenAI chat completion deployment as shown in the exhibit. Users are reporting that the assistant sometimes refuses to answer questions that are clearly within the scope of the provided data. What is the most likely issue?

Accepted Answer

The system message encourages the model to err on the side of caution, leading to false-negative refusals.. The system message likely contains overly cautious language (e.g., 'only answer if you are certain' or 'do not speculate'), which causes the model to refuse answering even when the data clearly supports the response. This is a known behavior in Azure OpenAI chat completions where the system message's tone and constraints directly influence refusal rates, leading to false-negative refusals.

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The system message explicitly prohibits making up information, which is correct behavior.

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The system message does not include instructions to use the provided data.

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The temperature parameter is set too high, causing the model to hallucinate.

Question 5

You are implementing a generative AI solution using Azure OpenAI. You need to ensure that the model's outputs do not contain certain inappropriate words or phrases. Which feature should you configure?

Accepted Answer

Content filters. Content filters in Azure OpenAI are specifically designed to detect and block inappropriate words or phrases in both prompts and completions. They operate at the service level, applying configurable severity thresholds for categories like hate, violence, sexual content, and self-harm, ensuring model outputs adhere to policy without requiring prompt engineering or data modifications.

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System message instructions

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Grounding with your data

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Max tokens limit

Question 6

You are building a multi-tenant application that uses Azure OpenAI. Each tenant has different content filtering requirements. How should you configure the solution to meet these requirements?

Accepted Answer

Create separate Azure OpenAI deployments for each tenant with their own content filter configurations.. Azure OpenAI content filters are configured at the deployment level, not at the application or API-call level. By creating separate deployments for each tenant, you can assign distinct content filter configurations (e.g., severity thresholds for hate, violence, self-harm) that are enforced server-side by Azure's content filtering service, ensuring isolation and compliance with each tenant's requirements.

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Use a single Azure OpenAI deployment and configure content filters at the application level.

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Configure different system messages for each tenant to enforce content policies.

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Pass the tenant ID in the API call and use a custom middleware to apply filters.

Question 7

You run the Azure CLI command shown in the exhibit. After a few minutes, the deployment fails with a quota error. What is the most likely cause?

Accepted Answer

The requested capacity of 10 exceeds the available quota for the gpt-4 model in that region.. The quota error indicates that the requested capacity (10 units) for the gpt-4 model exceeds the available quota in the target region. Azure OpenAI deployments require sufficient model-specific quota, which is region- and model-specific. The error is not related to SKU name validity, model version deprecation, or resource group existence.

Answer

The SKU name 'Standard' is invalid for Azure OpenAI deployments.

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The model version '0613' is deprecated and no longer available.

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The resource group name 'myResourceGroup' does not exist.

Question 8

You are using Azure OpenAI to generate product descriptions. You notice that the descriptions are often too similar to each other. Which parameter should you adjust to increase diversity?

Accepted Answer

Increase the temperature value.. Increasing the temperature parameter makes the model more creative by raising the probability of sampling lower-probability tokens, which increases diversity in the generated text. A higher temperature (e.g., 0.9) flattens the probability distribution, so the model is less likely to always pick the most probable next word, resulting in more varied outputs.

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Decrease the top_p value.

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Increase the max_tokens value.

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Increase the frequency_penalty value.

Question 9

Your company wants to use Azure OpenAI to generate code snippets. You need to ensure the generated code does not contain security vulnerabilities. Which approach should you take?

Accepted Answer

Include a system message that instructs the model to generate secure code following best practices.. Azure OpenAI's system messages allow you to set the context and behavior of the model, including instructing it to follow secure coding best practices (e.g., OWASP Top 10). This is the most direct and scalable way to influence the model's output at inference time without requiring post-processing or retraining. The system message acts as a persistent instruction that guides every response, making it the recommended approach for steering content generation.

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Scan the generated code with a static analysis tool after generation.

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Use content filters to block malicious code.

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Fine-tune the model on a dataset of secure code examples.

Question 10

You are designing a generative AI solution using Azure OpenAI Service with your own data indexed in Azure AI Search. Which THREE components are essential for the retrieval-augmented generation (RAG) pattern?

Accepted Answer

Data ingestion pipeline to Azure AI Search. A data ingestion pipeline is essential to load and index your data into Azure AI Search, enabling the retrieval step in RAG. Without this pipeline, the search index would have no data to query, breaking the retrieval-augmented generation pattern.

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Azure Functions for orchestration

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Azure API Management for rate limiting

Question 11

You are deploying a chat application using Azure OpenAI. The application should only answer questions based on a specific set of internal documents. Which THREE features should you use?

Accepted Answer

Azure AI Search index with the internal documents. Azure AI Search indexes allow you to ingest internal documents and perform vector or hybrid search over them. When integrated with Azure OpenAI, the search results are used as grounding context for the model, ensuring responses are based solely on your data.

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Content filters to block out-of-domain questions

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Fine-tuning the model on the internal documents

Question 12

You are testing an Azure OpenAI chat completion. The response shown in the exhibit is returned. What does the finish_reason of 'content_filter' indicate?

Accepted Answer

The model's response was blocked by the content filter.. The 'content_filter' finish_reason indicates that the Azure OpenAI content filtering system detected that the model's generated response violated one of the configured content policies (e.g., hate, violence, self-harm, sexual content). The response was therefore blocked before being returned to the user, and the finish_reason explicitly signals this filtering action rather than a normal completion or a stop due to token limits.

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There was a system error during processing.

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The user's prompt was flagged by the content filter.

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The model refused to answer due to insufficient data.

Question 13

You have deployed a chatbot using Azure OpenAI with a system message as shown. The chatbot sometimes provides incorrect answers that are not supported by the sources. What is the most likely cause?

Accepted Answer

The system message does not guarantee grounding; the model may still hallucinate.. A system message in Azure OpenAI provides instructions and context but does not enforce factual grounding. The model can still generate responses that are not supported by the provided sources (hallucination), especially if the system message is not explicitly designed to restrict the model to only use the given data. Grounding requires additional techniques like retrieval-augmented generation (RAG) or explicit constraints in the prompt.

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Content filters are incorrectly configured, allowing harmful content.

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The data ingestion pipeline has errors, so the sources are not available.

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The temperature parameter is too low, causing repetitive answers.

Question 14

You are developing a solution that uses Azure OpenAI to generate customer support responses. You want to prevent the model from repeating the same phrases. Which parameter should you adjust?

Accepted Answer

frequency_penalty. The frequency_penalty parameter (option D) is correct because it directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) decreases the probability of reusing tokens, making the output more diverse and less repetitive. This is specifically designed to address repetition in generative AI responses.

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top_p

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presence_penalty

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temperature

Question 15

You are developing a chat application that uses Azure OpenAI Service to answer customer queries. The solution must ensure that the model does not generate harmful or offensive content. Which Azure AI service should you configure?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is specifically designed to detect and filter harmful or offensive content in text and images, making it the appropriate service to integrate with an Azure OpenAI chat application to enforce content safety policies. It provides APIs for content moderation, including severity-based filtering for hate, self-harm, sexual, and violence categories, which directly addresses the requirement to prevent the model from generating harmful output.

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Azure AI Bot Service

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Azure AI Search

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Azure AI Language