AI-102 Implement generative AI solutions Practice Test 8 — 15 Questions

Question 1

A company wants to generate product descriptions for thousands of items using an Azure OpenAI GPT-4 model. They need to ensure the descriptions match a consistent brand voice. Which approach is most efficient and cost-effective?

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

Accepted Answer

Set a system message with brand voice guidelines and use few-shot examples. Setting a system message with brand voice guidelines and providing few-shot examples allows the GPT-4 model to consistently apply the desired tone and style across all product descriptions without retraining. This approach is efficient and cost-effective as it avoids the high compute and data preparation costs of fine-tuning, while still enabling precise control over output through in-context learning.

Answer

Write a separate prompt for each product category

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Use Azure OpenAI on your data with a vector database of brand guidelines

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Fine-tune a base model on existing product descriptions

Question 2

You are developing a solution that uses Azure Document Intelligence to extract data from invoices and then uses Azure OpenAI to summarize the extracted data. The solution occasionally produces summaries that omit key fields like the invoice total. What should you do to improve accuracy?

Accepted Answer

Define a structured prompt that explicitly requests each field and provide examples. The issue is that the summarization prompt lacks explicit instructions for which fields to include. By defining a structured prompt that explicitly requests each key field (e.g., invoice total, date, vendor) and providing examples, you guide the Azure OpenAI model to consistently extract and include those fields in the summary, reducing omission errors. This approach leverages prompt engineering to improve output reliability without changing model parameters or size.

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Set temperature to 0 to make the output more deterministic

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Use a larger model like GPT-4 instead of GPT-3.5

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Increase the max_tokens parameter

Question 3

Your Azure OpenAI application experiences high latency during peak hours. You have already scaled up the deployment to the maximum PTUs. What is the most effective next step to reduce latency?

Accepted Answer

Create multiple deployments across different regions and use Azure Traffic Manager to distribute requests. When PTU deployment is already maxed out, the bottleneck is the capacity of a single regional deployment. Distributing requests across multiple regional deployments via Azure Traffic Manager (using performance or geographic routing) spreads the load, reducing per-deployment contention and lowering latency. This approach leverages regional redundancy and global load balancing without requiring a model change or sacrificing quality.

Answer

Use Azure OpenAI's global deployment with the same PTU

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Switch from GPT-4 to GPT-3.5-turbo

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Increase the token limit per request

Question 4

You are building a generative AI solution using Azure Machine Learning prompt flow. The solution must allow business analysts without coding experience to modify prompts and evaluate different model versions. What should you do?

Accepted Answer

Use Azure Machine Learning prompt flow with the visual designer and variant management. Azure Machine Learning prompt flow provides a visual designer that enables non-technical users to modify prompts without coding, and its variant management feature allows them to evaluate different model versions side-by-side. This directly addresses the requirement for business analysts to iteratively refine prompts and compare model outputs in a controlled, no-code environment.

Answer

Provide the analysts with a Jupyter notebook using the OpenAI Python SDK

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Deploy a chatbot in Microsoft Copilot Studio and let analysts configure it

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Implement a custom web UI using Azure Static Web Apps and Azure Functions

Question 5

You deploy a chat application using Azure OpenAI Service. Users report that the model sometimes generates inappropriate content. You need to implement a safety system that can be customized for your organization's policies. What should you use?

Accepted Answer

Use Azure AI Content Safety with custom categories and severity thresholds. Azure AI Content Safety provides a customizable content moderation service that allows you to define custom categories and severity thresholds aligned with your organization's specific policies. This enables you to filter inappropriate content beyond the default filters, giving you granular control over what the model generates.

Answer

Use the content filter system in Azure OpenAI Studio

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Apply responsible AI templates from Azure AI Studio

Answer

Configure Microsoft Entra ID Conditional Access policies

Question 6

Your organization uses Azure OpenAI Service with a data source configured as 'Azure OpenAI on your data'. You notice that the responses include outdated information even though the underlying data source has been updated. What is the most likely cause?

Accepted Answer

The index in Azure Cognitive Search has not been refreshed. When using Azure OpenAI Service with 'Azure OpenAI on your data', the responses are generated by querying an Azure Cognitive Search index that contains your data. If the underlying data source has been updated but the responses still include outdated information, the most likely cause is that the index in Azure Cognitive Search has not been refreshed to reflect those updates. The model itself does not store or cache the data; it relies on the index at query time, so an outdated index directly leads to outdated responses.

Answer

The model is using a cached version of the prompt

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The data source is configured to sync only daily

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The Azure CDN is caching the responses

Question 7

You need to provide a generative AI solution that can answer questions based on a large set of PDF documents stored in Azure Blob Storage. The solution must support natural language queries and return citations from the documents. Which Azure service combination should you use?

Accepted Answer

Azure Cognitive Search and Azure OpenAI Service with 'on your data'. Azure Cognitive Search provides the indexing and retrieval capabilities for the PDF content, while Azure OpenAI Service with the 'on your data' feature enables natural language querying and generates answers grounded in the indexed documents, including citations. This combination directly supports the requirement to query a large set of PDFs in Azure Blob Storage and return citations.

Answer

Azure Machine Learning and Azure Kubernetes Service

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Azure AI Bot Service and Azure Functions

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Azure AI Document Intelligence and Azure AI Translator

Question 8

Which TWO are valid ways to manage cost when using Azure OpenAI Service in a production application?

Accepted Answer

Use a smaller model like GPT-3.5-turbo instead of GPT-4 for simpler tasks. Using a smaller model like GPT-3.5-turbo for simpler tasks directly reduces the per-token cost compared to GPT-4, which is significantly more expensive. Azure OpenAI Service charges based on model tier and token usage, so selecting the appropriate model for the task complexity is a primary cost management strategy.

Answer

Fine-tune the model to reduce the number of examples needed in prompts

Answer

Increase the temperature parameter to 1.0

Answer

Provision more PTUs to get a lower rate per token

Question 9

Which TWO statements about Azure OpenAI Service content filters are true?

Accepted Answer

They can be configured with severity levels (low, medium, high). Azure OpenAI Service content filters can be configured with severity levels (low, medium, high) to control the strictness of filtering for each content category. This allows administrators to fine-tune the filter sensitivity based on their application's risk tolerance and compliance requirements.

Answer

They only filter the output of the model

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They cannot be customized for specific use cases

Answer

They are bypassed when using PTU deployments

Question 10

You are building a chatbot for a retail company using Azure OpenAI Service. The chatbot must provide product recommendations based on customer preferences. To ensure the chatbot does not generate harmful or inappropriate responses, you need to implement a content filtering solution. What should you use?

Accepted Answer

Azure OpenAI Service content filtering. Azure OpenAI Service content filtering is the correct choice because it provides built-in, configurable filters that block harmful or inappropriate content at the model level, directly within the Azure OpenAI endpoint. This ensures that the chatbot's product recommendations remain safe without requiring external services, as the filtering is applied to both input prompts and output completions based on severity levels for categories like hate, violence, and self-harm.

Answer

Azure AI Content Safety

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Azure AI Search

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Microsoft Purview Information Protection

Question 11

You are deploying a generative AI solution using Azure OpenAI Service. The solution must comply with data residency requirements that mandate all data processing and storage remain within a specific geographic region. You need to configure the service to meet these requirements. What should you do?

Accepted Answer

Deploy the Azure OpenAI Service resource in the required region. Deploying the Azure OpenAI Service resource in the required region ensures that all data processing and storage occur within that geographic boundary, as Azure OpenAI Service is a regional resource that processes and stores data only in the region where it is provisioned. This directly satisfies data residency requirements because the service's infrastructure, including model inference and training data, remains within the selected Azure region. No additional routing or filtering is needed because the resource itself is bound to the region.

Answer

Use a custom endpoint that routes traffic to the required region

Answer

Select a model that is only available in the required region

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Configure content filtering to block data leaving the region

Question 12

You are developing a generative AI application that uses Azure OpenAI Service to summarize large documents. The application experiences high latency when processing requests. You need to reduce the latency without changing the model. What should you do?

Accepted Answer

Reduce the max_tokens parameter in the API request. Reducing the max_tokens parameter limits the length of the generated response, which directly reduces the processing time required by the Azure OpenAI Service to produce the output. Since latency is caused by the model generating a long sequence of tokens, capping the output tokens decreases the number of autoregressive decoding steps, thereby lowering response time without altering the underlying model.

Answer

Increase the temperature parameter

Answer

Increase the top_p parameter

Answer

Increase the max_tokens parameter

Question 13

You are using Azure OpenAI Service to generate code snippets for a development team. You notice that the generated code sometimes contains security vulnerabilities. You need to minimize the risk of generating insecure code while maintaining productivity. What should you do?

Accepted Answer

Use system messages to instruct the model to prioritize security. System messages in Azure OpenAI Service allow you to set the context and behavior of the model, including instructing it to prioritize security when generating code. This approach directly influences the model's output without requiring retraining or sacrificing flexibility, making it the most effective way to reduce security vulnerabilities while maintaining productivity.

Answer

Fine-tune the model on a dataset of secure code

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Set the temperature parameter to 0

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Disable content filtering to allow more flexibility

Question 14

You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only answer questions related to the company's products and policies. It should refuse to answer off-topic questions. You need to implement this restriction effectively. What should you do?

Accepted Answer

Implement Retrieval Augmented Generation (RAG) with your product and policy data. Retrieval Augmented Generation (RAG) grounds the model's responses in your specific product and policy data by retrieving relevant documents from a vector database (e.g., Azure Cognitive Search) and injecting them into the prompt. This ensures the chatbot can only answer questions that have matching context in your data, and it naturally refuses off-topic queries because no relevant documents are retrieved, allowing the system to return a default refusal message. RAG provides a dynamic, data-driven boundary without modifying the underlying model.

Answer

Fine-tune the model on a dataset of product-related conversations

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Use a system message that instructs the model to stay on topic

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Set the temperature parameter to 0 to reduce randomness

Question 15

You are deploying a generative AI solution using Azure OpenAI Service. You need to monitor the usage and costs associated with the service. What should you use?

Accepted Answer

Azure Monitor. Azure Monitor is the correct choice because it provides detailed metrics, logs, and alerts for Azure OpenAI Service usage, including token consumption, request counts, and latency. This data is essential for tracking costs and usage patterns, as Azure OpenAI charges based on tokens processed. Azure Monitor integrates directly with the service to surface these operational metrics.

Answer

Microsoft Purview

Answer

Azure Advisor

Answer

Azure Cost Management