AI-300 Free Practice Questions — Page 4

Question 31

DRAG DROP - A team deploys a classification model to production and scores incoming customer data daily. After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy. You need to determine whether data drift is occurring and if it is, identify the appropriate actions. Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each 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.

Question 32

DRAG DROP - A team operates a generative AI-powered customer support assistant built on Microsoft Foundry. The application serves users globally and supports both real-time chat interactions and batch summarization jobs. The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases. The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity. You need to select the performance metrics that meet the requirements. Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric 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 33

DRAG DROP - You have an existing GitHub repository containing Azure Machine Learning project files. You need to clone the repository to your Azure Machine Learning shared workspace file system. 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. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Question 34

DRAG DROP - A team performs interactive experimentation during development. The team also runs scalable jobs for model training. The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace. You need to configure compute targets that support each workload. Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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 35

HOTSPOT - You review the following Azure CLI command and the relevant Bicep excerpt. (Non-relevant sections are omitted.) You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Question 36

DRAG DROP - A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production. When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems. You need to configure the monitoring solution to meet the requirements. Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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 37

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 on 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: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data. Does the solution meet the goal?

Question 38

An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control. The organization requires models that meet the following requirements: Model behavior aligns with the task being performed. Data handling aligns with internal governance policies. Operational complexity and cost are justified by workload needs. You need to select the foundation model options that meet the requirements. Which three models can you select? Each correct answer presents a complete solution. Choose three. NOTE: Each correct selection is worth one point.

Question 39

DRAG DROP - A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load. You need to enable performance observability. 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 40

HOTSPOT - A company is creating an internal tool that summarizes long meeting transcripts and extracts action items. The model must: Process text inputs up to 200k tokens long. Generate concise summaries in seconds. Support interactive testing before integration into the app. You need to select, deploy, and test a model that supports summarization with low latency. How should you complete the configuration plan? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.