Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 2: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
- A. Grounded ness
- B. Fluency
- C. Textual similarity
- D. Coherence
Correct Answer: B 🗳️
Explanation: Only visible for PrepAwayExam members. You can sign-up / login (it's free).
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully Solution: Add the environment parameter.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
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.
Correct Answer:
Explanation:
Azure Machine Learning infrastructure can be described as Infrastructure as Code using ARM templates, Bicep, or Terraform. To make provisioning fully automated, two separate concerns must be addressed. First, the trigger: a CI/CD system like GitHub Actions or Azure DevOps Pipelines must detect changes to IaC template files in the repository, configured through a branch-push or pull-request trigger. Second, the execution: the CI/CD pipeline runs the provisioning commands such as az ml workspace create or bicep deploy non-interactively using a service principal or managed identity, eliminating the need for a human to run scripts manually. Together, these two mechanisms deliver the GitOps-style automation that modern MLOps practices require, ensuring every template change automatically results in a consistent, auditable infrastructure update.
Microsoft Learn Reference Topic: Automate Azure Machine Learning with GitHub Actions - CI/CD for ML infrastructure
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.
Correct Answer:
Explanation:
Microsoft ' s observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment - Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path - without actual production traffic, there is no latency data to observe.
Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence - instrument, deploy, visualize - is the canonical Microsoft path to production AI performance observability.
Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing
You create an Azure Machine Learning workspace
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
How should you complete ten code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Correct Answer:
Explanation:
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