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Instant Download Microsoft : AI-300 Questions & Answers as PDF & Test Engine
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Sep 22, 2026
- No. of Questions: 189 Questions and Answers
- Download Limit: Unlimited
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Topic 2: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering |
| Topic 3: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Topic 4: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
| Topic 5: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You create an Azure Machine Learning workspace and install the MLflow library.
You need to log different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.
- A. Use API key authentication for all model endpoints.
- B. Disable all inbound network access.
- C. Deploy the Microsoft Foundry resource in a separate Azure subscription.
- D. Configure a managed virtual network for the Microsoft Foundry resource.
- E. Disable public network access to the Microsoft Foundry resource.
Correct Answer: B,D 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?
- A. Store prompts as versioned files in a Git repository.
- B. Persist prompts in Azure Blob Storage with folder-level organization.
- C. Embed prompts directly in application configuration files.
- D. Register prompts as datasets in the Azure Machine Learning workspace.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace named workspace!.
You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2.
You must transform the Python code into a YAML specification that can be processed by the pipeline service.
You need to import the Python library that provides the transformation functionality.
Which Python library should you import?
- A. sklearn
- B. azure.ai.ml.entities
- C. azure.ai ml.automl
- D. mldesigner
Correct Answer: D 🗳️
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
In Azure ML command jobs, MLflow automatically creates a run context, so explicit start_run is optional.
The training script must call mlflow.log_param to record each hyperparameter for the run. If the script calls this for each hyperparameter, the requirement for capturing parameters per run is met. The script must also call mlflow.log_metric to record numeric metrics such as accuracy or loss, optionally indexed by training step. Metrics and parameters logged inside an Azure ML command job are automatically associated with the correct experiment and run - no manual specification of experiment or run ID is required. If all three elements (run context, parameter logging, metric logging) are present in the script, all experiment tracking statements are True. If any element is absent, the corresponding statement is False and the tracking requirement is not met.
Microsoft Learn Reference Topic: Log metrics, parameters, and artifacts during Azure Machine Learning training runs with MLflow
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