AI-300 Practice Test

Question 26

A team develops and manages a conversational assistant by using Microsoft Foundry. The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded. You need to automate evaluations as part of CI. What should you configure?

1. Blocklist applied to the model endpoint
2. Content filter configured in warning mode
3. Retrieval chunking strategy
4. GitHub Actions workflow that executes the runs

Question 27

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 recommend an experiment-tracking strategy that ensures consistent experiment results. What should you recommend?

1. Azure Machine Learning job output logs
2. MLflow experiment tracking
3. Application Insights logs
4. Azure Monitor alerts

Question 28

A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment. The team working on the model must ensure the following: Changes in input data distribution are detected. Appropriate actions are triggered when predefined thresholds are exceeded. You need to configure monitoring to meet the requirements. Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 29

You create a binary classification model. You use the Fairlearn package to assess model fairness. You must eliminate the need to retrain the model. You need to implement the Fairlearn package. Which algorithm should you use?

1. fairiearn.reductions.ExponentiatedGradient
2. fairlearn.preprocessing.CorrelationRemover
3. fairlearn.reductions.GridSearch
4. fairlearn.postprocessing.ThresholdOptimizer

Question 30

A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application. Users report intermittent failures and unexpected responses when calling the endpoint. You need to identify the appropriate troubleshooting action for each reported issue. Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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.