AI-102 Implement generative AI solutions Practice Test 2 — 15 Questions

Question 1

Which THREE factors should you consider when selecting a model for a generative AI solution on Azure?

Accepted Answer

Cost per token and deployment options.. Cost per token directly impacts operational expenses in pay-per-use models like GPT-4 on Azure OpenAI, and deployment options (e.g., global vs. regional, provisioned throughput) affect availability and scalability. These are fundamental considerations when operationalizing a generative AI solution.

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Number of transformer layers in the model.

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Training data source and licensing.

Question 2

Which TWO Azure services can be used together with Azure OpenAI to implement a Retrieval-Augmented Generation (RAG) solution?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is correct because it can serve as a vector database for storing and querying embeddings generated by Azure OpenAI. When combined with Azure AI Search, which provides hybrid search (vector + keyword) and indexing capabilities, it enables a complete RAG pipeline where relevant documents are retrieved and passed as context to the OpenAI model for grounded generation.

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Azure Blob Storage

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Azure SQL Database

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Azure Functions

Question 3

You are a machine learning engineer at a large retail company. The company has thousands of product descriptions that need to be updated regularly. They currently use a manual process. You propose using Azure OpenAI to generate new descriptions based on product attributes. You have a dataset of existing product descriptions and attributes stored in an Azure SQL Database. The solution must be cost-effective, scalable, and must not require retraining the model. You need to design the solution. You have the following options:

Option A: Use Azure OpenAI with few-shot learning by including examples in the prompt for each product. Deploy the model on an Azure Kubernetes Service (AKS) cluster for high throughput.

Option B: Use Azure OpenAI with prompt templates that include product attributes and call the API for each product. Use Azure Logic Apps to orchestrate the workflow and store results back to Azure SQL Database.

Option C: Fine-tune a custom model on the existing product descriptions and deploy it as a managed endpoint. Use Azure Data Factory to batch process all products.

Option D: Use Azure OpenAI with the batch API to generate descriptions for all products at once, using a single prompt that lists all products and attributes. Store the batch output in Azure Blob Storage and then import into Azure SQL Database.

Which option should you choose?

Accepted Answer

Option B. (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.

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Option C

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Option D

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Option A

Question 4

You are a cloud solution architect at a legal firm. The firm needs to automate the summarization of legal documents. They have a large corpus of past case summaries and legal documents stored in Azure Blob Storage. They want to use Azure OpenAI to generate summaries for new documents. The solution must ensure that the generated summaries are accurate and do not contain hallucinated legal facts. The firm also requires that the solution be serverless and minimize operational overhead. You need to design the solution.

Option A: Use Azure OpenAI with a system message that instructs the model to be accurate. Deploy the model as a web app on Azure App Service and call it from Azure Functions triggered by new blob uploads.

Option B: Use Azure OpenAI with Retrieval-Augmented Generation (RAG) by indexing the past case summaries in Azure AI Search. Use Azure Functions to process new documents, retrieve relevant cases, and pass them as context to the model. Store summaries in Azure Cosmos DB.

Option C: Fine-tune an Azure OpenAI model on the past case summaries and deploy it as a managed endpoint. Use Azure Logic Apps to trigger summarization when new blobs are added.

Option D: Use Azure OpenAI with the chat API and provide the entire document in the prompt. Use Azure Container Instances to run a service that calls the API and writes summaries back to Blob Storage.

Which option should you choose?

Accepted Answer

Option B. It uses Retrieval-Augmented Generation (RAG) with Azure AI Search to ground the model's output in verified past case summaries, directly addressing the requirement to avoid hallucinated legal facts. The serverless architecture is achieved via Azure Functions triggered by blob uploads, minimizing operational overhead, while storing summaries in Azure Cosmos DB provides a scalable, low-latency output store.

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Option A

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Option D

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Option C

Question 5

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it provides built-in content moderation APIs that detect and filter harmful or offensive text and images, including hate speech, violence, self-harm, and sexual content. Integrating this service with the Azure OpenAI chatbot ensures that user inputs and model outputs are screened in real time, preventing the generation of inappropriate responses.

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Azure Bot Service

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Azure Cognitive Search

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Azure Form Recognizer

Question 6

A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.

Answer

Increase the top_p parameter

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Increase the max_tokens parameter

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Increase the frequency_penalty parameter

Question 7

A developer wants to integrate a pre-built AI model that can extract key information from invoices, such as vendor name, invoice date, and total amount. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents like invoices, including fields such as vendor name, invoice date, and total amount. It uses pre-built models trained on common document types, making it ideal for this use case without requiring custom training.

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Azure AI Language

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Azure OpenAI Service

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Azure Cognitive Search

Question 8

An organization is deploying a conversational AI solution using Azure OpenAI. They want to ensure the model's responses are grounded in their own knowledge base documents to reduce hallucinations. Which approach should they implement?

Accepted Answer

Integrate Azure Cognitive Search for retrieval-augmented generation (RAG). Retrieval-Augmented Generation (RAG) with Azure Cognitive Search allows the model to dynamically retrieve relevant chunks from the organization's knowledge base documents at inference time. This grounds responses in authoritative, up-to-date content, directly reducing hallucinations by providing factual context rather than relying solely on the model's parametric memory.

Answer

Fine-tune the model on the knowledge base documents

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Implement Azure AI Content Safety filters

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Use prompt engineering to instruct the model to only use the knowledge base

Question 9

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

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Use Azure AI Document Intelligence to extract text from PDFs before indexing

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Deploy Azure AI Content Safety to filter responses

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Fine-tune the GPT model on the PDF content

Question 10

You are a data scientist at a healthcare company. You have deployed a GPT-4 model using Azure OpenAI to answer patient inquiries about medical conditions. The model is configured with temperature=0.3 and max_tokens=200. Recently, the compliance team flagged that some responses contain contradictory information compared to the official medical guidelines. You need to ensure the model's answers align strictly with the provided medical documents (stored as PDFs in Azure Blob Storage). You have access to Azure Cognitive Search and Azure AI Document Intelligence. The solution must minimize hallucinations and not require retraining the model. What should you do?

Accepted Answer

Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.. It uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.

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Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.

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Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.

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Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.

Question 11

A company is deploying a generative AI solution using Azure OpenAI Service to generate product descriptions. The solution must comply with responsible AI principles, specifically ensuring that generated content does not include harmful or offensive language. Which Azure AI service feature should they implement to automatically filter the output?

Accepted Answer

Enable the default content filtering system in Azure OpenAI Service.. Azure OpenAI Service includes a built-in default content filtering system that automatically screens generated outputs for harmful or offensive language, aligning with responsible AI principles. This system operates at the service level without additional configuration, making it the simplest and most direct way to filter product descriptions for compliance.

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Configure Prompt Shields in Azure AI Content Safety.

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Use Azure AI Content Safety with a custom category severity threshold.

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Use groundedness detection in Azure AI Content Safety.

Question 12

A developer is using Azure OpenAI to generate code snippets. The developer needs to ensure that the generated code does not contain security vulnerabilities. Which TWO actions should the developer take? (Choose two.)

Accepted Answer

Include examples of secure coding practices in the prompt.. Including examples of secure coding practices in the prompt (few-shot prompting) directly guides the model to follow those patterns, reducing insecure code generation. Option D is also correct because Azure OpenAI's content filtering feature can be configured to block code patterns that match known vulnerabilities (e.g., SQL injection, buffer overflows), providing an additional safety layer. Options B (high max_tokens), C (fine-tuning on insecure code), and E (system message) are not effective or counterproductive.

Answer

Set the max_tokens parameter to a high value to allow longer outputs.

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Fine-tune the model on a dataset containing examples of insecure code.

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Add a system message that instructs the model to never generate insecure code.

Question 13

Refer to the exhibit. The developer is using Azure OpenAI with data sources (preview) to ground the model on a custom dataset. The assistant response includes a citation [^1]. However, the developer notices that the citation does not appear in the final output displayed to the user. What is the most likely cause?

Accepted Answer

The 'include_contexts' parameter in the API call is set to false.. The 'include_contexts' parameter controls whether citation metadata from the data sources is included in the final output. When set to false, the API suppresses the citation markers (e.g., [^1]) even though the underlying context was used to generate the response. This is a preview feature of Azure OpenAI with your own data, where the citation rendering is explicitly gated by this parameter.

Answer

The citation format [^1] is not supported in Azure OpenAI with data sources.

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The system message overrides the citation rendering.

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The 'context' object in the assistant response is not being passed back in the prompt for the next turn.

Question 14

Drag and drop the steps to configure a multi-region disaster recovery for Azure Cognitive Services into the correct order.

Accepted Answer

Create resources in both regions, then enable geo-replication, then configure failover routing, then set up monitoring, then test failover. Start by creating resources in both regions, then set up replication, configure failover routing, monitor health, and test.

Answer

Create resources, then configure failover routing, then enable geo-replication, then set up monitoring, then test failover

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Create resources, then enable geo-replication, then set up monitoring, then configure failover routing, then test failover

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Create resources, then enable geo-replication, then configure failover routing, then test failover, then set up monitoring

Question 15

Drag and drop the steps to build and deploy a custom Azure AI Document Intelligence model into the correct order.

Accepted Answer

Step 1: Gather and label documents, Step 2: Create the Azure AI Document Intelligence resource, Step 3: Train the model, Step 4: Test the model, Step 5: Publish the model. First gather and label documents, create the resource, train the model, test, and publish.

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Step 1: Create the Azure AI Document Intelligence resource, Step 2: Gather and label documents, Step 3: Train the model, Step 4: Test the model, Step 5: Publish the model

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Step 1: Gather and label documents, Step 2: Train the model, Step 3: Create the Azure AI Document Intelligence resource, Step 4: Test the model, Step 5: Publish the model

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Step 1: Gather and label documents, Step 2: Create the Azure AI Document Intelligence resource, Step 3: Test the model, Step 4: Train the model, Step 5: Publish the model