AI-300 Free Practice Questions — Page 3

Question 21

DRAG DROP - A customer-facing web application uses a foundational model deployed through Microsoft Foundry. A new model version must be introduced and validated without disrupting production traffic. You need to deploy the new version by using a safe promotion strategy. 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 22

You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data. You must use a Responsible AI dashboard to assess the model. You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard. What should you do first?

Question 23

DRAG DROP - An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads. The organization has the following workload requirements: • One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance. • One workload must have variable performance and be optimized for cost-efficient operation. You need to select a global deployment type for each workload. Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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 24

A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning. The tuning process must run multiple training trials without manually modifying the training script for each run. You need to automate hyperparameter tuning for the training job. What should you do?

Question 25

You are authoring a notebook in Azure Machine Learning studio. You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only. You need to install the packages. Which magic function should you use?

Question 26

HOTSPOT - A biomedical research company plans to enroll people in an experimental medical treatment trial. You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity. The model returns different performance metrics for people from different ethnic groups. You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature. Which technique and constraint should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 27

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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be accessible from the same terminal session. You need to perform the task that must be completed before you can add the new kernel. Solution: Create an environment. Does the solution meet the goal?

Question 28

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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be accessible from the same terminal session. You need to perform the task that must be completed before you can add the new kernel. Solution: Delete the Python 3.8 - AzureML kernel. Does the solution meet the goal?

Question 29

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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be accessible from the same terminal session. You need to perform the task that must be completed before you can add the new kernel. Solution: Delete the Python 3.6 - AzureML kernel. Does the solution meet the goal?

Question 30

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 a solution to address Fabrikam Inc.’s limited rollback capability. Which deployment approach should you recommend?