AI-102 Implement generative AI solutions Practice Test 10 — 15 Questions

Question 1

You need to monitor usage and costs of your Azure OpenAI Service deployments. Which Azure tool should you use?

Accepted Answer

Azure Cost Management + Billing. Azure Cost Management + Billing is the correct tool for monitoring usage and costs of Azure OpenAI Service deployments because it provides detailed cost analysis, budget tracking, and usage reports across all Azure services. It allows you to set budgets, create cost alerts, and analyze spending patterns specifically for OpenAI model deployments, including per-model and per-region cost breakdowns.

Answer

Azure Monitor

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Azure Service Health

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Azure Advisor

Question 2

You are deploying a generative AI application. You have the JSON configuration above. You need to ensure that the model stops generating output at the end of each sentence. The current configuration uses a stop sequence of "
". What should you do?

Accepted Answer

Change the stop sequence to [".", "!" , "?"]. The goal is to stop generation at the end of each sentence. A sentence can end with a period (.), exclamation mark (!), or question mark (?). The current stop sequence of "
" only stops at newline characters, which does not guarantee sentence boundaries. By changing the stop sequence to an array containing [".", "!", "?"], the model will halt output whenever it generates any of these punctuation marks, ensuring each sentence is complete.

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Reduce the maxTokens to 500.

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Set the topP to 0.5.

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Set the temperature to 0.

Question 3

Which TWO actions can you take to mitigate the risk of generating harmful content when using Azure OpenAI Service? (Choose two.)

Accepted Answer

Set a system message that instructs the model to avoid harmful outputs.. Setting a system message instructs the model to avoid harmful outputs, guiding its behavior. Option D is correct because Azure AI Content Safety filters automatically detect and block harmful content. Option B is wrong because fine-tuning on safe examples may not fully prevent harmful generations; content safety is more reliable. Option C is wrong because deploying in multiple regions does not directly affect content safety. Option E is wrong because increasing maxTokens does not mitigate harmful content risk; it only controls response length.

Answer

Fine-tune the model on a dataset of safe examples.

Answer

Deploy the model in multiple regions.

Answer

Increase the maxTokens parameter to allow longer responses.

Question 4

Which THREE components are required to implement a Retrieval-Augmented Generation (RAG) solution with Azure OpenAI Service? (Choose three.)

Accepted Answer

An embedding model (e.g., text-embedding-ada-002). An embedding model like text-embedding-ada-002 is essential for converting user queries and document chunks into dense vector representations. These vectors enable semantic similarity search in a vector database, which is the core retrieval step in RAG. Without embeddings, the system cannot match user intent to relevant content.

Answer

A fine-tuned model

Answer

Azure AI Content Safety

Question 5

Which TWO Azure AI services can be used to build a conversational chatbot that uses generative AI? (Choose two.)

Accepted Answer

Azure AI Bot Service. Azure AI Bot Service (B) provides a dedicated platform for building, deploying, and managing conversational chatbots, integrating with channels like Microsoft Teams and Slack. Azure OpenAI Service (E) enables generative AI capabilities by providing access to large language models (e.g., GPT-4) that can generate human-like responses, making it ideal for powering the conversational intelligence of a chatbot.

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Azure AI Search

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Azure AI Translator

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Azure AI Language

Question 6

You are reviewing an ARM template for deploying Azure OpenAI Service. The template includes a deployment for gpt-35-turbo with a capacity of 100. You need to ensure that the deployment uses provisioned throughput instead of standard. What should you modify?

Accepted Answer

Change the sku name to 'ProvisionedManaged'.. To use provisioned throughput (PTU) with Azure OpenAI Service, you must set the SKU name to 'ProvisionedManaged' in the ARM template. The default SKU is 'Standard', which uses pay-per-token consumption. Changing the SKU name to 'ProvisionedManaged' tells the resource provider to allocate dedicated throughput capacity for the deployment, ensuring consistent latency and throughput regardless of other workloads.

Answer

Remove the raiPolicyName property.

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Increase the capacity to 200.

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Change the model format to 'GPT-4'.

Question 7

You run the above PowerShell command to check the responsible AI policy applied to an Azure OpenAI Service deployment. The output shows 'MyPolicy'. You need to verify that the policy blocks hate speech. What should you do?

Accepted Answer

Navigate to Azure AI Content Safety and view the policy 'MyPolicy'.. Azure AI Content Safety is the dedicated service for managing and viewing the details of content filtering policies, including custom policies like 'MyPolicy'. The PowerShell command only confirms the policy name is applied to the deployment; to verify that the policy blocks hate speech, you must inspect the policy's configuration (e.g., severity thresholds for hate categories) within the Azure AI Content Safety portal. The Azure Portal deployment properties only show the policy name, not its rules.

Answer

Use the Azure Portal to review the deployment's properties.

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Run Get-AzCognitiveServicesAccountDeployment with -ExpandProperties.

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Use Azure Monitor to check the deployment's logs.

Question 8

You are using Azure OpenAI Service to generate marketing copy. You notice that the output sometimes contains factual inaccuracies about your company's products. Which action can you take to improve factual accuracy?

Accepted Answer

Include relevant product information in the system message.. Including relevant product information in the system message provides the model with authoritative context that grounds its responses in factual data. The system message acts as a persistent instruction set that the model uses to shape its outputs, reducing reliance on its internal training data which may be outdated or incomplete. This technique, known as 'grounding,' directly improves factual accuracy by supplying the model with the specific facts it needs to generate correct marketing copy.

Answer

Lower the temperature to 0.

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Increase the maxTokens to 4000.

Answer

Add a stop sequence to limit output.

Question 9

You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only respond based on the company's product documentation and should not generate answers outside that scope. Which approach should you use?

Accepted Answer

Use Azure OpenAI On Your Data with a search index built from the documentation.. Azure OpenAI On Your Data allows you to ground the model's responses on a specific set of documents by connecting it to a search index (e.g., Azure Cognitive Search) built from the product documentation. This ensures the model retrieves relevant chunks from the index and generates answers solely based on that content, preventing out-of-scope responses without modifying the underlying model.

Answer

Implement content filters to block responses not found in the documentation.

Answer

Fine-tune a GPT-4 model on the product documentation.

Answer

Use prompt engineering with a system message instructing the model to only answer from the documentation.

Question 10

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?

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

Answer

Azure Functions

Question 11

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.

Answer

Increase the frequency_penalty parameter.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.

Question 12

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.

Answer

Azure OpenAI Service

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Azure AI Vision

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Azure AI Document Intelligence

Question 13

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 14

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.

Answer

Azure Monitor

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Application Insights

Answer

Azure AI Language

Question 15

Your organization uses Azure OpenAI Service to generate code snippets. You want to log all user prompts and model responses for auditing purposes. What should you configure?

Accepted Answer

Enable diagnostic settings in Azure OpenAI Service to send logs to a Log Analytics workspace.. Azure OpenAI Service's diagnostic settings allow you to stream platform logs—including user prompts and model responses—to a Log Analytics workspace. This is the native, built-in mechanism for capturing and analyzing request/response data for auditing, without needing additional services or custom middleware.

Answer

Use Azure API Management to log requests and responses.

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Store logs in Azure Key Vault.

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Use Azure AI Search to index the prompts and responses.