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

Question 1

Match each Azure Cognitive Search skill to its capability.

Accepted Answer

Entity Recognition: Identifies and categorizes entities in text as people, places, organizations, etc.. Common Azure Cognitive Search skills include Entity Recognition, Key Phrase Extraction, OCR (for image text extraction), and Sentiment Analysis. The correct matches are A and B. Options C and D are mismatched definitions.

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OCR: Detects language and translates text to a target language.

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Sentiment Analysis: Detects the dominant language of a document.

Question 2

Match each Azure Bot Service channel to its description.

Accepted Answer

Direct Line: Enables communication between your bot and your own client application.. The correct matches are: Direct Line for custom client communication, Web Chat for embeddable chat, Microsoft Teams for team collaboration, and Facebook Messenger for Messenger integration. Common mistakes include confusing Direct Line and Web Chat definitions.

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Direct Line: Embeds a chat control in a web page.

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Microsoft Teams: Enables communication between your bot and your own client application.

Question 3

A company uses Azure OpenAI Service to generate product descriptions. They notice that the descriptions sometimes contain factually incorrect information. Which strategy should they use to reduce hallucinations?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) by grounding prompts with a knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base, providing factual context that directly reduces hallucinations. By retrieving relevant documents and injecting them into the prompt, the model generates responses based on verified information rather than relying solely on its parametric memory, which is the primary cause of factual inaccuracies in Azure OpenAI Service.

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Increase the temperature parameter to 1.0.

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Reduce the max_tokens parameter to limit output length.

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Add a system message instructing the model to be more careful.

Question 4

You are designing a generative AI solution that uses Azure OpenAI GPT-4 to answer customer support questions. The solution must comply with Microsoft's Responsible AI principles, particularly transparency and accountability. Which implementation approach best meets these requirements?

Accepted Answer

Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.. It directly addresses Microsoft's Responsible AI principles of transparency and accountability. Enabling content filtering (via Azure AI Content Safety) ensures harmful outputs are blocked, logging all interactions provides an audit trail for accountability, and including a disclaimer that responses are AI-generated satisfies transparency by clearly informing users they are interacting with an AI system.

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Use the model without any modifications, and have a human review all responses.

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Fine-tune the model on a curated dataset of support tickets and disable content filtering.

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Use the default model deployment and rely on the model's inherent safety.

Question 5

You need to generate realistic synthetic data for training a machine learning model while ensuring the data does not contain personally identifiable information (PII). Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models (e.g., GPT-4) that can create realistic synthetic data by learning patterns from training data. Crucially, these models can be configured to avoid memorizing or reproducing PII, and you can apply content filters and data masking to ensure the generated output is free of personally identifiable information.

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Azure AI Search

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Azure AI Document Intelligence

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Azure AI Language

Question 6

A company uses Microsoft Copilot for Microsoft 365 to automate email responses. They want to ensure that the Copilot responses comply with their data governance policies and do not expose sensitive information. What should they configure?

Accepted Answer

Microsoft Purview Data Loss Prevention (DLP) policies. Microsoft Purview Data Loss Prevention (DLP) policies are designed to detect and prevent the accidental sharing of sensitive information, such as credit card numbers or personally identifiable information (PII), across Microsoft 365 services. By configuring DLP policies, the company can scan Copilot-generated email responses for sensitive data patterns and block or warn before the email is sent, ensuring compliance with data governance policies.

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Microsoft Entra ID conditional access policies

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Microsoft Intune app protection policies

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Microsoft Sentinel analytics rules

Question 7

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.. 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 8

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 9

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.. 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 10

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.. 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 11

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.. Retrieval-Augmented Generation (RAG) uses an Azure AI Search index to retrieve relevant policy documents and then generates answers based solely on that content, ensuring responses are grounded in the provided documents. Option A is wrong because the model's pre‑existing knowledge is generic and not specific to the company's internal policies, so answers may be inaccurate or unsupported. Option B is wrong because fine‑tuning a GPT model on the policy documents can still lead to hallucination; the model might generate plausible‑sounding but ungrounded content not directly from the documents. Option C is wrong because prompt engineering alone cannot force the model to ignore its training data; without retrieval, the model may incorporate external knowledge or fabricate information.

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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 12

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 13

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 14

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 15

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.