www.cert4prep.com/ Microsoft AI-300 Operationalizing Machine Learning and Generative AI Solutions Version: 8.1 Questions & Answers DEMO PDF (Preview content before you buy) Check the full version using the link below www.cert4prep.com/exam/ai-300 Unlock Full Features Stay Updated: 90 days of free exam updates Zero Risk: 30-day money-back policy Instant Access: Download right after purchase Always Here: 24/7 customer support team Page 1 of 6 www.cert4prep.com/exam/ai-300 www.cert4prep.com/ Question 1. (Multi Select) A team manages an Azure Machine Learning workspace and deploys a model to an endpoint. A deployed online endpoint shows inconsistent response times during periods of high traffic. You need to identify potential performance degradation. Which three metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose three A: Feature count B: Requests per minute C: Connections active D: Dataset size E: Request latency Answer: B, C, E Explanation: During high traffic, the question is whether the endpoint is keeping up with demand. Requests per minute (B) tells you the actual request load on the endpoint, letting you correlate traffic spikes with degradation. Connections active (C) reveals whether the endpoint's connection pool is saturating — too many concurrent connections without adequate scaling causes queuing and timeouts. Request latency (E) is the gold-standard measure of user-perceived performance; rising latency under load is the clearest signal of degradation. Feature count (A) is a model-design attribute, not a runtime performance metric. Dataset size (D) is a training-time concern unrelated to endpoint performance. Azure Machine Learning online endpoints expose these metrics through Azure Monitor, and Microsoft recommends configuring alert rules on latency and request rate thresholds for all production endpoints. Microsoft Learn Reference Topic: Monitor Azure Machine Learning online endpoints – Azure Monitor metrics for managed endpoints Question 2. (ORDERLIST) You manage an Azure Machine Learning workspace. You train a model named model1. Page 2 of 6 www.cert4prep.com/exam/ai-300 You must identify the features to modify for a differing model prediction result. You need to configure the Responsible Al (RAI) dashboard for model1. 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. A: Add the explanation component to the Responsible AI Insights dashboard. B: Add the error analysis component to the Responsible AI Insights dashboard. C: Add the causal component to the Responsible AI Insights dashboard. D: Load and configure the Responsible Al Insights dashboard constructor component. E: Add the counterfactuals component to the Responsible Al Insights dashboard. F: Use the Gather Responsible Al Insights dashboard component to present the dashboard. Answer: D, E, F Question 3. (DRAGDROP) A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources. Provisioning must be triggered by changes in the templates and executed without manual intervention. You need to automate resource provisioning. Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. 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. Page 3 of 6 www.cert4prep.com/exam/ai-300 Answer: Question 4. (Single Select) You need to standardize how Fabrikam Inc. manages machine learning assets. Which action should you perform first? A: Register assets in the Azure Machine Learning registry. B: Create a shared Azure Machine Learning workspace. C: Deploy a managed online endpoint. D: Create a new Microsoft Foundry project. Answer: B Explanation: In Azure Machine Learning, a workspace is the top-level resource that acts as a centralized hub for every ML activity. It stores experiments, pipelines, datasets, models, environments, and compute targets all in one place. Before you can register assets or deploy endpoints, the workspace itself must exist and be shared across the team. Fabrikam's core challenge is inconsistent experiment tracking, ad-hoc versioning, and no standardized deployment path — all symptoms of the absence of a single, governed workspace. Option A (registering assets) is a subsequent action only possible after the workspace exists. Option C (online endpoint) is a deployment concern, not an asset-management foundation. Option D (Foundry project) is appropriate for generative AI workloads, not for the traditional ML Page 4 of 6 www.cert4prep.com/exam/ai-300 pipeline standardization described here. The workspace is the prerequisite that makes every other governance action possible. Microsoft Learn Reference Topic: Azure Machine Learning workspaces – Microsoft Learn: Manage Azure Machine Learning workspaces Question 5. (HOTSPOT) Answer: Page 5 of 6 www.cert4prep.com/exam/ai-300 www.cert4prep.com/ Need more info? Check the link below: www.cert4prep.com/exam/ai-300 Thanks for Being a Valued Cert4Prep User! Guaranteed Success Pass Every Exam with Cert4Prep. Save $15 instantly with promo code LEARN15 Sales: sales@cert4prep.com Support: support@cert4prep.com Page 6 of 6 www.cert4prep.com/exam/ai-300