AI-102 Implement generative AI solutions Practice Test 12 — 15 Questions

Question 1

You need to deploy a generative AI model that can be used by multiple applications within your organization. The model must support real-time inference with low latency. Which Azure service should you use?

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

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides managed access to powerful generative AI models like GPT-4, which are optimized for real-time inference with low latency through provisioned throughput units (PTUs) and regional deployment options. This service is specifically designed for generative AI workloads, offering REST API endpoints that support streaming responses and sub-second latency for single-turn interactions, making it ideal for multiple applications requiring consistent, low-latency responses.

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Azure AI Search

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Azure Machine Learning real-time endpoint

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Azure Functions

Question 2

You are using Azure OpenAI Service to generate product descriptions. You notice that the model occasionally outputs descriptions that contain factual inaccuracies about product specifications. You want to reduce these hallucinations without changing the model. What should you do?

Accepted Answer

Provide the product specifications in the prompt and use the system message to instruct the model to base answers on them.. Providing the product specifications directly in the prompt and using the system message to instruct the model to base its answers on them grounds the generation in factual data, reducing hallucinations. This technique, known as 'grounding' or 'retrieval-augmented generation' (RAG), does not modify the model itself but constrains its output to the provided context, which is the only way to reduce factual inaccuracies without changing model parameters.

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Increase the frequency_penalty parameter.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.

Question 3

Your company wants to build a custom generative AI model that generates architectural designs. The model should be trained on the company's proprietary dataset of floor plans and designs. Which Azure service should you use?

Accepted Answer

Azure Machine Learning. Azure Machine Learning is the correct service because it provides a comprehensive platform for training custom generative AI models using your own proprietary datasets. It supports deep learning frameworks like PyTorch and TensorFlow, enabling you to build, train, and deploy a custom generative model for architectural designs, whereas Azure OpenAI Service is limited to pre-trained models and fine-tuning, not custom training from scratch.

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Azure OpenAI Service

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Azure AI Vision

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Azure AI Document Intelligence

Question 4

You are developing a generative AI application that uses Azure OpenAI Service. You want to ensure that the application does not generate offensive content. Which Azure service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and filter offensive, inappropriate, or harmful content in text and images. For a generative AI application using Azure OpenAI, this service can be integrated to review prompts and completions in real time, ensuring that generated outputs comply with content policies and do not contain hate speech, violence, or other offensive material.

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Azure AI Bot Service

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Azure AI Language

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Azure AI Search

Question 5

You have deployed a generative AI model using Azure Machine Learning. The model is used for generating financial reports. You need to monitor the model's performance and detect data drift in the input data. What should you use?

Accepted Answer

Azure Machine Learning data drift monitoring. Azure Machine Learning data drift monitoring is the correct choice because it is specifically designed to detect statistical changes in input data over time, which is critical for generative AI models used in financial reporting where data distributions can shift due to market changes or new regulations. It compares the current input data distribution against a baseline dataset using metrics like Wasserstein distance or Population Stability Index, and triggers alerts when drift exceeds a threshold. This ensures the model's outputs remain reliable and compliant, which is a core requirement for generative AI solutions in regulated industries.

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Azure Monitor

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Application Insights

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Azure AI Language

Question 6

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 7

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 8

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 9

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 10

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 11

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 that explicitly instructs the model to avoid bias and providing few-shot examples of unbiased summaries is the most direct and cost-effective way to steer the model's behavior without retraining. This approach leverages prompt engineering to influence the model's output distribution while preserving the ability to generate detailed 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 12

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 13

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 14

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 15

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.