- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Sep 19, 2026
- Q & A: 189 Questions and Answers
Microsoft AI-300 actual dump : Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
You create an Azure Data Lake Storage Gen2 stowage account named storage1 containing a file system named fsi and a folder named folder1.
The contents of folder1 must be accessible from jobs on compute targets in the Azure Machine Learning workspace.
You need to construct a URl to reference folder1.
How should you construct the URI? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Question #2
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
- A. Data Zone Batch
- B. Developer
- C. Data Zone Standard
- D. Global Batch
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Dumps members. You can sign-up / login (it's free).
Question #3
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
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:
Correct sequence:
* Install the Foundry SDK project client locally.
* Configure safety evaluators.
* Submit an evaluation to the Microsoft Foundry project in the cloud.
The first step is to install and configure the Microsoft Foundry SDK project client . Microsoft documents the Foundry project client as the programmatic entry point for authenticating to a Foundry project and accessing its evaluation capabilities. The client typically uses DefaultAzureCredential, avoiding embedded credentials while enabling access to project resources.
Next, configure the safety evaluators that correspond to the risks that must be measured. Microsoft Foundry provides built-in safety evaluators for categories including Violence, Sexual content, Self-harm, and Hate
/Unfairness . These evaluators analyze generated responses and return structured safety assessments rather than relying on subjective manual review.
Finally, submit the evaluation to the Microsoft Foundry project in the cloud . Current Foundry evaluation workflows define the evaluator configuration, create an evaluation, and start an evaluation run in the project.
Results are persisted in Foundry for comparison, auditing, and CI/CD quality gates.
Uploading evaluation data is unnecessary because the scenario explicitly states that the dataset is already registered . Microsoft documentation specifically instructs users to skip dataset upload when a registered dataset already exists. Free-form text upload is also inappropriate because structured evaluation datasets use supported schemas such as JSONL or CSV.
Study Guide Reference: Implement generative AI quality assurance and observability - Foundry evaluations, safety evaluators, evaluation datasets, cloud evaluation runs, and harmful-content measurement.
Question #4
You manage an Azure Machine Learning workspace.
You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?
- A. mlflow.log_artifacts(my_dict >
- B. mlflow.log metrics(my diet)
- C. mlflow.log_artifact(my_dict)
- D. mlflow.log_metric( " my_metric " , my_dict)
Correct Answer: D 🗳️
Question #5
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
A system requesting predictions synchronously during customer interactions needs sub-second responses, while a system submitting files with millions of records can tolerate minutes of processing time. For real-time synchronous serving, a Managed Online Endpoint provisions a persistent always-on container behind an HTTPS REST endpoint that returns predictions within milliseconds. For large-batch asynchronous scoring, a Batch Endpoint accepts a data asset input, distributes scoring across a compute cluster, and writes results back to storage. Online endpoints support auto-scaling based on request volume and traffic splitting. Batch endpoints are invoked on-demand or on a schedule, automatically provisioning and de-provisioning compute, keeping costs low for intermittent large jobs. Each deployment type is purpose-built for its usage pattern and should not be swapped.
Microsoft Learn Reference Topic: Deploy and score models with managed online endpoints and batch endpoints - Azure Machine Learning
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