AI-102 Implement generative AI solutions Practice Test 11 — 15 Questions

Question 1

You are using Azure OpenAI Service to generate marketing copy. You have a requirement to reduce the cost of inference without significantly impacting output quality. Which parameter should you adjust?

Accepted Answer

Decrease the max_tokens.. Decreasing max_tokens directly reduces the number of tokens generated per API call, which lowers the compute cost because Azure OpenAI charges per token (both input and output). Since the requirement is to reduce inference cost without significantly impacting output quality, reducing max_tokens is the most direct and effective parameter. It caps the response length, preventing unnecessarily verbose output while preserving the model's ability to generate high-quality, concise copy.

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Increase the frequency_penalty.

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Decrease the temperature.

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Increase the top_p.

Question 2

You need to deploy a generative AI model that can generate images from text descriptions. Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to advanced generative AI models like DALL-E, which are specifically designed to generate images from natural language text descriptions. This service offers pre-trained models that can create high-quality images based on textual prompts, making it the correct choice for this task.

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Azure Machine Learning

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Azure AI Vision

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Azure AI Language

Question 3

Which TWO actions should you take to ensure that a generative AI model deployed on Azure Machine Learning is compliant with data privacy regulations?

Accepted Answer

Implement data masking during preprocessing.. Options A and C are correct. A: Data masking during preprocessing helps anonymize sensitive data before it is used, reducing privacy risks. C: Differential privacy adds noise to the training process, preventing the model from memorizing individual data points. B is wrong because storing data in a separate Azure region does not inherently address privacy compliance; it may help with data residency but not the core privacy protection. D is wrong because encrypting the model at rest with Azure Key Vault is a security measure, not a privacy compliance measure. E is wrong because logging raw input data to Azure Monitor could expose sensitive information, violating privacy.

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Store all training data in a separate Azure region.

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Encrypt the model at rest using Azure Key Vault.

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Log all raw input data to Azure Monitor for auditing.

Question 4

Which THREE factors should you consider when choosing between Azure OpenAI Service and Azure Machine Learning for deploying a generative AI model?

Accepted Answer

Latency requirements: Azure OpenAI may offer lower latency for standard models.. Azure OpenAI Service provides managed endpoints for pre-trained models like GPT-4, which are optimized for low-latency inference out of the box. In contrast, Azure Machine Learning requires you to deploy your own containerized model, which can introduce additional network and compute overhead, making Azure OpenAI the better choice when sub-100ms response times are critical for standard generative AI tasks.

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Integration with Microsoft Purview for data governance.

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Ability to scale to thousands of concurrent requests.

Question 5

Which TWO options are valid ways to reduce the cost of using Azure OpenAI Service?

Accepted Answer

Use provisioned throughput with reserved capacity.. Provisioned throughput with reserved capacity allows you to commit to a specific amount of throughput (tokens per minute) for a fixed period, typically one month or one year, in exchange for a significant discount compared to pay-as-you-go pricing. This reduces per-token costs when you have predictable workloads.

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Increase the temperature parameter.

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Increase the max_tokens parameter to get longer responses.

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Enable content filtering on all requests.

Question 6

Refer to the exhibit. You are deploying a GPT-4 model using Azure OpenAI Service. The deployment uses the Standard scale type. Which statement is true about this deployment?

Accepted Answer

The deployment uses pay-as-you-go pricing and global rate limits.. The Standard scale type in Azure OpenAI Service uses pay-as-you-go pricing, where you are billed based on the number of tokens processed. It also enforces global rate limits (e.g., tokens per minute) that apply across all deployments in the region, rather than providing dedicated capacity. Option D correctly identifies these characteristics.

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The model version is not specified and will default to the latest.

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Content filtering is disabled for this deployment.

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The deployment uses provisioned throughput with reserved capacity.

Question 7

Refer to the exhibit. You are deploying a generative AI model as an online endpoint in Azure Machine Learning. You receive complaints that the endpoint returns 503 errors during peak hours. What is the most likely cause?

Accepted Answer

The manual scale setting with 2 instances may be insufficient for peak traffic.. 503 errors during peak hours indicate that the endpoint is overwhelmed by the request volume. With manual scaling set to only 2 instances, the compute capacity is insufficient to handle the increased traffic, causing the service to reject requests. Azure Machine Learning online endpoints require sufficient instance count or autoscaling to absorb traffic spikes.

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The request timeout of 30 seconds is too short.

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The environment variable MODEL_CACHE_SIZE is set too low.

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The model version is not specified correctly.

Question 8

You are a generative AI engineer at a financial services company. The company uses Azure OpenAI Service to generate investment summaries. You have deployed a GPT-4 model with a content filter set to 'Low' for hate speech. The model frequently generates summaries that include biased language against certain demographics. You need to reduce biased outputs while maintaining the ability to generate detailed financial analysis. You cannot afford to retrain the model. You have the following options: A) Change the content filter severity to 'High' for all categories, B) Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt, C) Use the Azure AI Language service to detect bias in the output and regenerate if bias is found, D) Deploy a different model like GPT-3.5 which has less bias. Which course of action should you take?

Accepted Answer

Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt.. Adding a system message and examples in the prompt (few-shot learning) can effectively reduce biased outputs without retraining. Option A is wrong because increasing content filter severity may block legitimate financial analysis content. Option C is wrong because detecting bias after generation and regenerating is inefficient and may still produce biased output. Option D is wrong because GPT-3.5 may also exhibit bias and may not provide the same quality of financial analysis.

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Use the Azure AI Language service to detect bias in the output and regenerate if bias is found.

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Change the content filter severity to 'High' for all categories.

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Deploy a different model like GPT-3.5 which has less bias.

Question 9

You are developing a chat application that uses Azure OpenAI Service to answer customer queries. You need to ensure that the model does not generate responses containing internal company policies or confidential information. Which approach should you use?

Accepted Answer

Set the system message to instruct the model not to include confidential information.. Setting the system message in Azure OpenAI Service allows you to define high-level behavioral instructions for the model, such as prohibiting the disclosure of internal policies or confidential information. This approach leverages the model's instruction-following capability without requiring retraining or external filtering, making it a direct and effective guardrail for content generation.

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Use Azure AI Search with index filtering to exclude documents with confidential info.

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Fine-tune the model on a dataset that excludes confidential information.

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Configure Azure AI Content Safety with custom categories for confidential terms.

Question 10

Your organization uses Azure AI Document Intelligence to extract data from invoices. The extraction accuracy for total amounts is low. You have a labeled dataset of 500 invoices. You need to improve the model's accuracy for the 'total amount' field. What should you do?

Accepted Answer

Create a custom neural model and train it with the labeled dataset.. Azure AI Document Intelligence's custom neural model is specifically designed to improve extraction accuracy for fields like 'total amount' by training on labeled datasets. Unlike the prebuilt invoice model, a custom neural model learns the unique layout and variations in your invoices, directly addressing low accuracy for a specific field. Training with 500 labeled invoices provides sufficient data to fine-tune the model's extraction capabilities.

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Add additional predefined models for invoice processing.

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Enable OCR enhancement to improve text recognition.

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Increase the confidence threshold for the total amount field.

Question 11

You are deploying a conversational AI solution using Microsoft Copilot Studio. The solution must provide responses based on data from an internal knowledge base stored in SharePoint. Which feature should you configure?

Accepted Answer

Enable Generative Answers and add SharePoint as a data source.. Microsoft Copilot Studio natively supports Generative Answers, which allows the AI to dynamically generate responses from specified data sources without requiring a separate QnA or custom question-answering service. By adding SharePoint as a data source, the solution can directly query the internal knowledge base stored in SharePoint and produce contextually relevant answers. This is the most straightforward and integrated approach within Copilot Studio for this scenario.

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Configure Azure AI Language with custom question answering.

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Create an Azure Bot Service with a QnA Maker knowledge base.

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Use Azure OpenAI Service with data integration (Bring Your Own Data).

Question 12

You are building a solution to generate product descriptions using Azure OpenAI Service. You need to ensure that the output adheres to a specific tone (professional, friendly) and length (50-100 words). Which parameter should you adjust?

Accepted Answer

Set the system message with instructions about tone and length.. The system message in Azure OpenAI Service is specifically designed to set the overall behavior and context for the model, including tone and length constraints. By providing instructions like 'Respond in a professional and friendly tone, and keep the output between 50 and 100 words,' the model will adhere to these guidelines throughout the conversation. This is the primary mechanism for controlling qualitative aspects of the output, as opposed to parameters that control randomness or token limits.

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Configure max_tokens to limit response length.

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Modify the top_p parameter.

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Adjust the temperature parameter.

Question 13

You are using Azure AI Studio to deploy a fine-tuned model for code generation. After deployment, you notice that the model returns nonsensical code snippets. You need to diagnose the issue. What should you check first?

Accepted Answer

Evaluate the model performance on a held-out test dataset.. Evaluating the model on a held-out test dataset is the standard first step to diagnose whether the fine-tuned model has generalized properly or is overfitting. If the model produces nonsensical code, the most likely cause is poor training data quality or overfitting, and a held-out evaluation provides a quantitative measure (e.g., perplexity, BLEU score, or exact match) to confirm this before investigating other factors.

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Verify that the training data is in JSONL format.

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Test the base model without fine-tuning to compare outputs.

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Check the deployment's rate limits and quotas.

Question 14

You need to generate realistic synthetic data using Azure OpenAI Service to train a machine learning model. The data must be diverse and cover edge cases. Which approach should you use?

Accepted Answer

Use prompt engineering with detailed instructions to generate varied examples.. Prompt engineering with detailed instructions allows you to explicitly control the diversity, structure, and edge-case coverage of generated synthetic data without requiring a pre-existing dataset. By crafting system messages and user prompts that specify variations in attributes, formats, and boundary conditions, you can produce a wide range of realistic examples that mimic real-world distributions, which is essential for training robust machine learning models.

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Fine-tune the model on a small dataset of real examples.

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Use Azure OpenAI embeddings to generate similar data points.

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Set a high temperature parameter only.

Question 15

You are implementing a RAG (Retrieval-Augmented Generation) solution using Azure AI Search and Azure OpenAI Service. The solution is returning answers that are not relevant to the user query. What is the most likely cause?

Accepted Answer

The relevance score threshold is set too low.. A low relevance score threshold in Azure AI Search allows documents with low semantic or vector similarity to be returned as results. When these poorly matched documents are passed to Azure OpenAI Service for answer generation, the model may produce answers that are not relevant to the user query, as the retrieved context is noisy or unrelated.

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The max_tokens parameter is set too high.

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The chunk size is too small.

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The index includes too many documents.