AI-300 Free Practice Questions

Question 1

A team is building a Retrieval-Augmented Generation (RAG) system. The team observes that the retrieved documents are often irrelevant or incomplete. You need to improve retrieval accuracy. What should you adjust?

Question 2

DRAG DROP - You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment. The deployment process must ensure that proper validation and control is maintained. You need to promote the fine-tuned model from development to production. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Question 3

You are preparing training data for a fine-tuning job in Microsoft Foundry. Real production conversations cannot be used due to compliance requirements. You need to generate synthetic interaction data that can be used for fine-tuning a generative model. What should you do?

Question 4

HOTSPOT - You manage a Microsoft Foundry project. You are evaluating two RAG solutions. When generating answers, the solutions display the following results: • The first solution displays low completeness and low utilization. • The second solution displays low completeness and high utilization. You need to address the issues found during evaluation. Which action should you perform first for each issue? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 5

Case Study - This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study. To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study. A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it. To start the case study - To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the "Question" button to return to the question. Background - Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States. Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions. Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues. Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk. Current Environment - Fabrikam Inc. operates a single Azure subscription that has the following components: • Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets • Azure AI Search indexing curated analytical documents and reference materials • A small set of Python-based training scripts maintained by data scientists • Azure OpenAI Service with deployed foundational models • A Microsoft Foundry resource for building a RAG-based solution Evaluation data has manually defined expected responses. The current challenges faced by the data science team include the following: • Model training jobs are run manually from notebooks. • Experiment tracking is inconsistent • Model versions are registered without standardized metadata. • Deployment is performed manually by data scientists, with limited rollback capability. • The team has no standardized evaluation process for generative AI outputs. The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage. Business Requirements - Fabrikam Inc. has the following business requirements for the modernization initiative: • Provide a conversational interface that answers analytics questions by using internal documents and datasets. • Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant. • Enable repeatable and auditable model training and deployment processes. • Support experimentation to compare prompt strategies and fine-tuned models. • Align the model with the ranked preferences and optimize behavior for the long term. • Minimize disruption to existing analytics workloads during rollout. Technical Requirements - To support the business goals, Fabrikam Inc. identifies these technical requirements: • Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments. • Implement experiment tracking and model versioning for all training jobs. • Orchestrate training and evaluation by using pipelines rather than manually running notebooks. • Deploy traditional machine learning models with support for staged rollout and rollback. • Improve RAG-based solution output quality. • Use the existing evaluation datasets that are based on real data with input-output pairs. • Apply advanced fine-tuning techniques only when prompt engineering is insufficient Issues and Constraints - Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure. Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads. Problem Statement - Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities. You need to improve a GPT-5 model performance based on Fabrikam Inc.’s technical requirements. Which action should you perform first?

Question 6

DRAG DROP - You develop a Prompt flow in Microsoft Foundry project. You plan to use variants and invoke a custom API in the flow. You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts. Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

Question 7

A product team is building a customer support assistant that must respond consistently across multiple channels. Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy. The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model. You need to design prompts that improve response quality while remaining flexible for future changes. Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.

Question 8

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You work in Microsoft Foundry with a prompt flow. You must manually evaluate prompts and compare results across prompt variants. You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation. Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation. Does the solution meet the goal?

Question 9

DRAG DROP - A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK. The evaluation dataset is registered in the Microsoft Foundry environment. You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Question 10

DRAG DROP - You have a Microsoft Foundry project with a connected Azure OpenAI Service model. You have a set of text files stored locally on your computer. You must set up a flow that will generate responses based on the content of your local files. You need to implement a solution. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.