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Microsoft AI-300 Q&A - in .pdf
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Nov 10, 2026
- Q & A: 189 Questions and Answers
- Convenient, easy to study.
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- Updated: Nov 10, 2026
- Q & A: 189 Questions and Answers
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- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Nov 10, 2026
- Q & A: 189 Questions and Answers
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model.
Which parameter should you use?
- A. properties
- B. description
- C. model_framework
- D. cags
Correct Answer: A 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #2
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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.
The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #3
You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.'s issues, constraints, and technical requirements.
What should you implement?
- A. Dedicated compute clusters per experiment
- B. Training jobs that run on a single shared compute cluster
- C. Managed compute targets with autoscaling
- D. Fixed-size compute cluster
Correct Answer: C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #4
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
What should you do first?
- A. Create an experiment.
- B. Create a compute cluster.
- C. Create an attached compute resource.
- D. Select a model.
Correct Answer: C 🗳️
Question #5
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
* name= " mlcluster1''
* size= " STANDARD.DS3.v2 "
* min_instances=1
* maxjnstances=4
* tier= " dedicated "
The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? 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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Viewing questions 1-5 out of 189 questions.
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