AI-102 Implement generative AI solutions Practice Test 1 — 15 Questions

Question 1

A company wants to generate personalized product descriptions for its e-commerce site using Azure OpenAI. They need to ensure the model's output adheres to brand guidelines and does not generate prohibited content. Which approach should they use?

Accepted Answer

Use a system message with brand guidelines and apply content filtering.. Option A is correct because using a system message allows you to embed brand guidelines directly into the conversation context, instructing the model on tone, style, and prohibited content. Azure OpenAI's content filtering provides an additional safety layer by automatically detecting and blocking harmful or policy-violating outputs, ensuring compliance with both brand and regulatory requirements.

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Use prompt engineering with negative prompts and ignore content filtering.

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Provide few-shot examples in the user message and rely on the model's training.

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Fine-tune the model with brand guidelines and disable content filtering for performance.

Question 2

A healthcare startup is developing a chatbot that uses Azure OpenAI to answer patient questions. They need to ensure that the chatbot only uses information from their verified medical database and does not generate unsupported medical advice. What is the best approach?

Accepted Answer

Use Azure AI Search with vector search to retrieve relevant documents and pass them as context.. Option D is correct because it uses Azure AI Search with vector search to retrieve only relevant, verified documents from the medical database and passes them as context to the Azure OpenAI model. This grounds the model's responses in authoritative data, preventing it from generating unsupported medical advice. The retrieval-augmented generation (RAG) pattern ensures the chatbot answers are based on the provided context rather than the model's internal knowledge.

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Fine-tune a model on the medical database and deploy it.

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Embed the entire medical database in the system message.

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Rely on Azure OpenAI's content filtering to block unsupported advice.

Question 3

A developer wants to deploy a custom generative AI model using Azure Machine Learning. Which compute target should they choose for low-latency real-time inference?

Accepted Answer

Azure Kubernetes Service (AKS). Azure Kubernetes Service (AKS) is the correct compute target for low-latency real-time inference because it supports horizontal pod autoscaling, GPU acceleration, and can be configured with a low-latency ingress controller (e.g., NGINX or Azure Application Gateway) to route inference requests directly to model containers. AKS also integrates with Azure Machine Learning's real-time inference endpoint, which uses a gRPC or HTTP-based scoring protocol to achieve sub-100ms response times.

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Local deployment

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Azure Batch

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Azure Functions

Question 4

A company uses Azure OpenAI to generate code snippets. They notice that the model sometimes produces code that uses deprecated APIs. They want to minimize this without retraining the model. What should they do?

Accepted Answer

Add a system message instructing the model to use only current, non-deprecated APIs.. Option C is correct because adding a system message in Azure OpenAI allows you to set high-level instructions that guide the model's behavior without retraining. By explicitly instructing the model to use only current, non-deprecated APIs, you leverage the system prompt's ability to influence output style and content, effectively reducing deprecated API usage in generated code snippets.

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Fine-tune the model on a dataset of recent code.

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Set the temperature parameter to 0 to reduce randomness.

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Provide a few-shot example of correct code in the prompt.

Question 5

A financial services firm wants to use Azure OpenAI to generate investment advice summaries. They must ensure that the model does not produce any advice that could be interpreted as personalized financial advice. What is the most effective strategy?

Accepted Answer

Use a system message that instructs the model to avoid personalized advice and apply strict content filtering.. Option B is correct because Azure OpenAI's system messages allow you to set the model's behavior and constraints at the conversation level, which is the most direct and effective way to enforce a policy like avoiding personalized financial advice. Combined with Azure's content filtering (which can block harmful or restricted content), this approach provides both instruction-based and filter-based guardrails without requiring model retraining or relying solely on example-based prompting.

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Set temperature to 0 and top_p to 0 to make outputs deterministic.

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Provide few-shot examples of disclaimers in the prompt.

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Fine-tune the model on a dataset of generic financial summaries.

Question 6

A developer wants to use Azure OpenAI to generate text from a prompt. Which parameter controls the diversity of the generated output?

Accepted Answer

temperature. Temperature is the parameter that directly controls the randomness or diversity of the generated output by scaling the logits before applying the softmax function. A higher temperature (e.g., 1.0) increases the probability of less likely tokens, producing more creative and varied responses, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused.

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presence_penalty

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frequency_penalty

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max_tokens

Question 7

A company is using Azure OpenAI to generate customer support responses. They want to ensure the model does not use any personally identifiable information (PII) in its outputs. What should they implement?

Accepted Answer

Use Azure AI Content Safety to filter PII from the output.. Azure AI Content Safety provides built-in PII detection and redaction capabilities that can automatically scan and filter sensitive information from model outputs. This is the most reliable approach because it operates as a post-processing filter, catching PII that the model might generate despite instructions. Fine-tuning, prompt engineering, and system messages are all fallible because they rely on the model's compliance rather than enforced filtering.

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Fine-tune the model on anonymized data.

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Use prompt engineering to instruct the model to redact PII.

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Use a system message instructing the model to avoid PII.

Question 8

A research lab wants to use Azure OpenAI to generate synthetic data for training a model. They need to generate a large volume of data quickly and cost-effectively. Which approach should they use?

Accepted Answer

Use the batch API to process requests asynchronously.. The batch API is designed for high-throughput, asynchronous processing of large volumes of requests, making it ideal for generating synthetic data at scale. It allows the lab to submit many prompts in a single batch, which Azure OpenAI processes efficiently, reducing both cost and time compared to real-time processing.

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Fine-tune a model to generate the data locally.

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Use the streaming API with multiple concurrent connections.

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Deploy a model on Azure Functions and call it in parallel.

Question 9

Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?

Accepted Answer

Use the batch API for non-real-time requests.. Option B is correct because the batch API allows you to submit non-real-time requests that are processed asynchronously, often at a lower cost per token compared to real-time inference. This is ideal for chatbot scenarios where some queries (e.g., historical analysis or bulk processing) do not require immediate responses, reducing overall Azure OpenAI consumption costs.

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Increase max_tokens to reduce the number of requests.

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Lower the temperature to 0 to reduce token usage.

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Fine-tune the model to reduce prompt length.

Question 10

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.. Option A is correct because 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 11

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 12

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. Option D is correct because it uses Azure OpenAI's batch API to process all product descriptions in a single asynchronous job, which is cost-effective (pay-per-page) and scalable without requiring model retraining. The batch output is stored in Azure Blob Storage and then imported into Azure SQL Database, meeting the requirement for a cost-effective, scalable solution that avoids retraining.

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Option C

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Option D

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Option A

Question 13

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. Option B is correct because 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 14

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 15

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.

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Increase the top_p parameter

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Increase the max_tokens parameter

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Increase the frequency_penalty parameter