AI-102 Implement generative AI solutions Practice Test 7 — 15 Questions

Question 1

You are deploying a generative AI model using Azure AI Foundry. The model must be accessible only from a specific virtual network. Additionally, you need to monitor all API calls for auditing. Which two configurations are required? (Choose two.)

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

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Enabling diagnostic settings to send logs to a Log Analytics workspace captures all API call details (e.g., request URI, response status, caller IP) for auditing and monitoring. This is the standard Azure method for collecting resource-level logs, and it works with Azure AI Foundry deployments to meet compliance and security requirements.

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Assign a managed identity to the model deployment.

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Enable public network access from selected IP addresses.

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Configure CORS to allow only the VNet's domain.

Question 2

You need to create a chatbot that uses Azure OpenAI to answer questions about your company's internal policies. The responses must be based only on the provided policy documents. Which approach should you use?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with an Azure AI Search index of the documents.. RAG (Retrieval-Augmented Generation) is the correct approach because it grounds the model's responses in your actual policy documents by retrieving relevant chunks from an Azure AI Search index at query time and injecting them into the prompt. This ensures the chatbot answers only from the provided documents, avoids hallucination, and keeps the source of truth external and updatable without retraining. Azure OpenAI's 'On Your Data' feature implements exactly this pattern using Azure AI Search as the vector/keyword store.

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Use the model's pre-existing knowledge about common policies.

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Fine-tune a GPT model on the policy documents.

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Use prompt engineering to instruct the model to only use policy knowledge.

Question 3

Refer to the exhibit. A developer received this response from an Azure OpenAI chat completion call. The prompt was "What is the capital of France?". The finish_reason is "stop". What does this indicate?

Accepted Answer

The model completed the response naturally.. The finish_reason 'stop' indicates that the model completed the response naturally, meaning it generated a complete answer to the prompt and reached a logical stopping point (e.g., the end of a sentence or the end of the generated text). This is the standard behavior for a successful completion where the model did not encounter any content filter, token limit, or other interruption.

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The response was truncated due to content filtering.

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The model stopped generating before the response was complete.

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The response reached the max_tokens limit.

Question 4

Refer to the exhibit. A developer runs this PowerShell script to call Azure OpenAI. The script fails with an authentication error. What is the most likely cause?

Accepted Answer

The script uses the wrong HTTP header; it should use 'api-key' instead of 'Authorization: Bearer'.. The script uses 'Authorization: Bearer' header, but Azure OpenAI requires the API key to be passed in the 'api-key' header. The 'Authorization: Bearer' header is used for Microsoft Entra ID token-based authentication, not for direct API key authentication. Since the script is using an API key (as indicated by the PowerShell script), the correct header is 'api-key'.

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The script uses the wrong HTTP method; it should use GET.

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The API version is incorrect.

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The endpoint URI is missing the resource name.

Question 5

A company uses Azure OpenAI to generate marketing copy. They want to ensure that the generated content does not contain offensive language. Which feature should they enable?

Accepted Answer

Enable content filtering at the deployment level.. Azure OpenAI provides built-in content filtering at the deployment level that automatically detects and blocks offensive or harmful language in both input prompts and generated outputs. This feature uses Microsoft's Responsible AI models to enforce safety policies without requiring custom code or manual review, making it the most reliable and scalable solution for preventing offensive content in marketing copy.

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Use DALL-E to generate images instead of text.

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Use a system message instructing the model to avoid offensive language.

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Enable diagnostic logging to review all outputs.

Question 6

You are using Azure AI Foundry to fine-tune a GPT-3.5 model on a dataset of customer service conversations. The fine-tuning job fails with an error indicating that the training data format is invalid. What is the most likely issue?

Accepted Answer

The training data is not in JSONL format with the correct structure.. Azure AI Foundry requires fine-tuning data to be in JSONL format with a specific structure: each line must be a JSON object containing a 'messages' array with 'role' and 'content' fields for system, user, and assistant turns. The error indicates the training data format is invalid, and the most likely cause is that the data is not in this required JSONL structure, as JSONL is the only accepted format for GPT-3.5 fine-tuning in Azure OpenAI Service.

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The training data is in CSV format instead of JSON.

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The training data contains only one conversation example.

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The training data does not include the assistant's responses.

Question 7

You need to generate an image of a cat wearing a hat using Azure OpenAI. Which model should you use?

Accepted Answer

DALL-E. DALL-E is the Azure OpenAI model specifically designed for generating images from natural language descriptions. It uses a diffusion-based architecture to create high-quality, original images based on text prompts, making it the correct choice for generating an image of a cat wearing a hat.

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Codex

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GPT-4

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Whisper

Question 8

Refer to the exhibit. An administrator runs this Azure CLI command to deploy a GPT-4 model in Azure AI Foundry. The command fails with an error that the deployment name already exists. What should the administrator do to resolve the issue?

Accepted Answer

Use a different deployment name or delete the existing deployment.. The error message indicates that a deployment with the same name already exists in the Azure AI Foundry workspace. In Azure AI Foundry, deployment names must be unique within a workspace. The correct resolution is to either choose a different deployment name or delete the existing deployment before re-running the command. This aligns with the Azure CLI behavior where resource names (including AI model deployments) must be unique per scope.

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Specify a different resource group.

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Remove the --sku-name parameter.

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Use a different model version.

Question 9

You need to generate a summary of a long article using Azure OpenAI. The article is 10,000 tokens long. What should you do to fit the article within the model's context window?

Accepted Answer

Split the article into smaller sections and summarize each section separately.. The article exceeds the model's context window (typically 4096 or 8192 tokens for GPT-3.5/4). Splitting the article into smaller sections and summarizing each separately allows you to process the entire content within the token limits, then combine the summaries for a final coherent output. This is a standard chunking strategy for long documents when using Azure OpenAI.

Answer

Increase the temperature parameter.

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Use a model with a smaller context window.

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Set max_tokens to a lower value.

Question 10

A developer uses the Azure OpenAI API to generate code. They want to ensure that the generated code is in Python. Which parameter should they set?

Accepted Answer

system message. The system message is used to set the behavior and context of the AI model, including specifying the desired output format or language. By setting the system message to 'You are a helpful assistant that always writes code in Python', the developer can instruct the model to generate Python code consistently. This parameter is part of the chat completions API and directly influences the model's persona and constraints.

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temperature

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top_p

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max_tokens

Question 11

You are building a generative AI solution using Azure AI Foundry. The solution must meet compliance requirements that require all model inputs and outputs to be auditable for a minimum of one year. What should you enable?

Accepted Answer

Diagnostic settings to capture request and response logs and store them in a storage account.. Enabling diagnostic settings for the Azure AI Foundry resource allows you to capture detailed request and response logs for all model interactions. By routing these logs to a storage account, you retain the data for the required one-year audit period, meeting compliance needs for full traceability of inputs and outputs.

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Azure Monitor alerts for unusual activity.

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Azure Monitor metrics for the Azure AI Foundry resource.

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Azure Monitor workbooks to visualize usage.

Question 12

A company uses Azure OpenAI to generate product descriptions. They want to ensure that the descriptions are consistent in style and tone. Which strategy should they use?

Accepted Answer

Provide a few examples of desired style in the prompt (few-shot learning).. Few-shot learning (option B) is the correct strategy because it directly controls style and tone by providing examples of desired output within the prompt. This leverages the model's in-context learning ability without modifying the underlying model weights, making it ideal for enforcing consistency without the cost and complexity of fine-tuning.

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Fine-tune the model on a dataset of product descriptions.

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Set max_tokens to a small value to limit output length.

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Increase the temperature to 1.0 for more creativity.

Question 13

You are deploying a generative AI model using Azure AI Foundry. The model must be accessible only from a specific virtual network. Additionally, you need to monitor all API calls for auditing. Which two configurations are required? (Choose two.)

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

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Enabling diagnostic settings to send logs to a Log Analytics workspace allows you to capture and audit all API calls made to the model deployment. This is essential for monitoring, security auditing, and compliance, as it records detailed telemetry such as request timestamps, caller IPs, and operation names. Option E is correct because disabling public network access and configuring a private endpoint ensures that the model is only accessible from within the specified virtual network, meeting the isolation requirement.

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Assign a managed identity to the model deployment.

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Configure CORS to allow only the VNet's domain.

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Enable public network access from selected IP addresses.

Question 14

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?

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 15

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.