AI-102 Implement generative AI solutions Practice Test 3 — 15 Questions

Question 1

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.. Option B is correct because 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 2

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

Question 3

A developer uses the Azure OpenAI SDK to generate code snippets. The generated code sometimes contains security vulnerabilities. What is the most effective way to mitigate this risk?

Accepted Answer

Include a system message that instructs the model to avoid insecure coding patterns.. Option D is correct because a system message sets the behavioral context for the model at inference time, instructing it to avoid insecure coding patterns without requiring retraining. This is the most direct and effective mitigation as it leverages the model's instruction-following capability to reduce vulnerabilities in generated code, aligning with Azure OpenAI's content filtering and safety system guidance.

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Set the temperature parameter to 0 to make the output deterministic.

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Post-process the generated code using a static code analysis tool.

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

Question 4

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.)

Accepted Answer

Enable diagnostic settings to send logs to a Log Analytics workspace.. Option A is correct because 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 5

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.. Option B is correct because RAG uses provided documents as source. Option A is wrong because fine-tuning on policies may still generate ungrounded responses. Option C is wrong because prompt engineering alone does not enforce grounding. Option D is wrong because the model's training data is generic.

Answer

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 6

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 7

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 Azure AD 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 8

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.. Option D is correct because 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.

Answer

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 9

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 10

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.

Answer

Codex

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GPT-4

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Whisper

Question 11

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.

Answer

Specify a different resource group.

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Remove the --sku-name parameter.

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Use a different model version.

Question 12

A company is developing a generative AI solution that must process sensitive customer data. They need to ensure that data remains within their Azure tenant and is not used to improve the base model. Which configuration is required in Azure OpenAI?

Accepted Answer

Opt out of abuse monitoring and data logging in the Azure OpenAI Studio.. Option C is correct because Azure OpenAI provides a data privacy setting that allows customers to opt out of abuse monitoring and data logging. When enabled, this ensures that prompts, completions, and any associated data are not stored or used by Microsoft to improve the base model, and all data remains within the customer's Azure tenant. This is the specific configuration required to meet the stated data residency and non-improvement requirements.

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Enable customer-managed keys for encryption.

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Use a private endpoint to restrict access to the service.

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Configure data residency to keep data in a specific region.

Question 13

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.. Option A is correct because 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 14

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 15

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.. Option D is correct because 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.