Microsoft AI-300 Questions & Answers - in .pdf
- Total Q&A: 159
- Update: Aug 08, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Q&A - Testing Engine
- Total Q&A: 159
- Update: Aug 08, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
- 1. Choose appropriate models and parameters
- 2. Tune prompts and generation settings
- Improve efficiency and cost-effectiveness
- 1. Manage resource utilization
- 2. Optimize inference and deployment
|
| Topic 2: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
- 1. Configure projects, connections, and security
- 2. Manage compute and deployment resources
- Implement infrastructure for generative AI workloads
- 1. Integrate with Azure services and tools
- 2. Design scalable and secure architecture
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
- 1. Track experiments and metrics
- 2. Create and manage pipelines
- Register, version, and package models
- 1. Manage model registry
- 2. Create reusable model packages
- Monitor and maintain models in production
- 1. Implement retraining and update workflows
- 2. Monitor data and model drift
- Deploy models to production
- 1. Deploy to real-time and batch endpoints
- 2. Configure deployment options and scaling
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
- 1. Track usage, performance, and errors
- 2. Implement logging and alerting
- Evaluate and test generative AI applications
- 1. Test for safety, accuracy, and relevance
- 2. Define evaluation metrics and criteria
|
| Topic 5: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
- 1. Configure workspace settings and security
- 2. Manage compute targets, datastores, and environments
- Implement infrastructure as code for Machine Learning
- 1. Use Bicep or Azure CLI to deploy resources
- 2. Automate infrastructure provisioning
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
A) Use the create_or_update method to change the tag of the image.
B) Change the description parameter of the environment configuration.
C) Use the Environment class to create a new version of the environment.
D) Modify the conda_file to specify the new version of the Docker image.
2. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?
A) Ignore schema differences
B) Use larger compute
C) Retrain manually when failure occurs
D) Add a data validation component in pipeline
3. You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
A) Same user identity
B) Same compute SKU
C) Same dataset name
D) Versioned datasets and environments
4. 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) Global Batch
C) Developer
D) Data Zone Standard
5. Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
Question # 1
Answer: A | Question # 2
Answer: D | Question # 3
Answer: D | Question # 4
Answer: D | Question # 5
Answer: Only visible for members |
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