Operationalizing Machine Learning and Generative AI Solutions (AI-300) Practice Test
Build your confidence for Operationalizing Machine Learning and Generative AI Solutions (AI-300). Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
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The Operationalizing Machine Learning and Generative AI Solutions (AI-300) certification validates the advanced skills required to design, deploy, and manage production-grade AI systems at enterprise scale. This credential is designed for AI engineers, MLOps practitioners, and cloud architects who are responsible for moving machine learning and generative AI workloads from experimentation into reliable, governed, and observable production environments. Candidates are assessed on their ability to architect end-to-end MLOps infrastructure, implement full model lifecycle management, and build GenAIOps pipelines that support continuous integration, delivery, and monitoring of large language model applications. The exam further evaluates competency in generative AI quality assurance, including evaluation frameworks, prompt and response observability, hallucination detection, and responsible AI safeguards. Finally, it tests the ability to optimize generative AI systems for latency, cost, throughput, and accuracy through techniques such as caching, retrieval augmentation tuning, model routing, and fine-tuning strategies. Earning this certification demonstrates that a professional can operationalize AI responsibly and at scale, bridging the gap between data science innovation and dependable production operations. It reflects current industry demand for practitioners who can govern AI systems, ensure quality, and deliver measurable business value across the AI lifecycle.
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Sample Questions
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122 of 123 answers carry a checkable reference.
You need to create a new pipeline in Azure Data Factory. Which steps should you take in Data Factory Studio?
A company is evaluating Microsoft Foundry and wants to understand how it is monetized. Which of the following best describes the pricing model?
An organization wants to adopt a modular approach for its machine learning workflows. Which outcome is a direct benefit of this approach?
When creating an endpoint with a system-assigned managed identity, what permissions are required for accessing the workspace storage account and container registry?
A team of data scientists wants to build many machine learning models quickly without sacrificing quality. Which approach should they use?
Exam insights and study advice
As organizations accelerate their adoption of machine learning and generative AI, the demand for professionals who can reliably operationalize these systems has become one of the most sought-after skill sets in the technology industry. The AI-300 certification signals to employers, clients, and peers that you possess verified expertise in the operational discipline that separates successful AI initiatives from stalled proofs of concept. Holding this credential positions you for roles such as MLOps engineer, AI platform architect, and generative AI operations specialist, and it demonstrates mastery of the governance, observability, and optimization practices that enterprises require before trusting AI in production. In a competitive market, this certification provides credible, industry-recognized evidence of your ability to deliver AI solutions that are scalable, measurable, and responsible.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.
01Implement machine learning model lifecycle and operations
Topics
- Configure experiment tracking with MLflow
- Use automated machine learning to explore optimal models
- Use notebooks for experimentation and exploration
- Automate hyperparameter tuning
- Run model training scripts
- Manage distributed training for large and deep learning models
- Implement training pipelines
- Compare model performance across jobs
- Package a feature retrieval specification with the model artifact
- Register an MLflow model
- Evaluate a model by using responsible AI principles
- Manage model lifecycle, including archiving models
- Deploy models as real-time or batch endpoints with managed inference options
- Test and troubleshoot model endpoints
- Implement progressive rollout and safe rollback strategies
- Detect and analyze data drift
- Monitor performance metrics of models deployed to production
- Configure retraining or alert triggers when thresholds are exceeded
Learning objectives
- Configure experiment tracking with MLflow
- Use automated machine learning to explore optimal models
- Use notebooks for experimentation and exploration
- Automate hyperparameter tuning
- Run model training scripts
- Manage distributed training for large and deep learning models
- Implement training pipelines
- Compare model performance across jobs
- Package a feature retrieval specification with the model artifact
- Register an MLflow model
- Evaluate a model by using responsible AI principles
- Manage model lifecycle, including archiving models
- Deploy models as real-time or batch endpoints with managed inference options
- Test and troubleshoot model endpoints
- Implement progressive rollout and safe rollback strategies
- Detect and analyze data drift
- Monitor performance metrics of models deployed to production
- Configure retraining or alert triggers when thresholds are exceeded
02Design and implement a GenAIOps infrastructure
Topics
- Create and configure Foundry resources and project environments
- Configure identity and access management with managed identities and role-based access control (RBAC)
- Implement network security and private networking configurations
- Deploy infrastructure using Bicep templates and Azure CLI
- Deploy foundation models by using serverless API endpoints and managed compute options
- Select appropriate models for specific use cases
- Implement model versioning and production deployment strategies
- Configure provisioned throughput units for high-volume workloads
- Design and develop prompts
- Create prompt variants and compare performance across different prompts
- Implement version control for prompts by using Git repositories
Learning objectives
- Create and configure Foundry resources and project environments
- Configure identity and access management with managed identities and role-based access control (RBAC)
- Implement network security and private networking configurations
- Deploy infrastructure using Bicep templates and Azure CLI
- Deploy foundation models by using serverless API endpoints and managed compute options
- Select appropriate models for specific use cases
- Implement model versioning and production deployment strategies
- Configure provisioned throughput units for high-volume workloads
- Design and develop prompts
- Create prompt variants and compare performance across different prompts
- Implement version control for prompts by using Git repositories
03Design and implement an MLOps infrastructure
Topics
- Create and manage a workspace
- Create and manage datastores
- Create and manage compute targets
- Configure identity and access management for workspaces
- Create and manage data assets
- Create and manage environments
- Create and manage components
- Share assets across workspaces by using registries
- Configure GitHub integration with Machine Learning to enable secure access
- Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
- Automate resource provisioning by using GitHub Actions workflows
- Restrict network access to Machine Learning workspaces
- Manage source control for machine learning projects by using Git
Learning objectives
- Create and manage a workspace
- Create and manage datastores
- Create and manage compute targets
- Configure identity and access management for workspaces
- Create and manage data assets
- Create and manage environments
- Create and manage components
- Share assets across workspaces by using registries
- Configure GitHub integration with Machine Learning to enable secure access
- Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
- Automate resource provisioning by using GitHub Actions workflows
- Restrict network access to Machine Learning workspaces
- Manage source control for machine learning projects by using Git
04Implement generative AI quality assurance and observability
Topics
- Create test datasets and data mapping for comprehensive model evaluation
- Implement AI quality metrics, including groundedness, relevance, coherence, and fluency
- Configure risk and safety evaluations for harmful content detection
- Set up automated evaluation workflows by using built-in and custom evaluation metrics
- Examine continuous monitoring in Foundry
- Monitor performance metrics, including latency, throughput, and response times
- Track and optimize cost metrics, including token consumption and resource usage
- Configure detailed logging, tracing, and debugging capabilities for production troubleshooting
Learning objectives
- Create test datasets and data mapping for comprehensive model evaluation
- Implement AI quality metrics, including groundedness, relevance, coherence, and fluency
- Configure risk and safety evaluations for harmful content detection
- Set up automated evaluation workflows by using built-in and custom evaluation metrics
- Examine continuous monitoring in Foundry
- Monitor performance metrics, including latency, throughput, and response times
- Track and optimize cost metrics, including token consumption and resource usage
- Configure detailed logging, tracing, and debugging capabilities for production troubleshooting
05Optimize generative AI systems and model performance
Topics
- Optimize retrieval performance by tuning similarity thresholds, chunk sizes, and retrieval strategies
- Select and fine-tune embedding models for domain-specific use cases and accuracy improvements
- Implement and optimize hybrid search approaches combining semantic and keyword-based retrieval
- Evaluate and improve RAG system performance by using relevance metrics and A/B testing frameworks
- Design and implement advanced fine-tuning methods
- Create and manage synthetic data for fine-tuning
- Monitor and optimize fine-tuned model performance
- Manage a fine-tuned model from development through production deployment
- Last updated on 03/05/2026
- Purpose of this document
- About the exam
- Skills measured
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
Learning objectives
- Optimize retrieval performance by tuning similarity thresholds, chunk sizes, and retrieval strategies
- Select and fine-tune embedding models for domain-specific use cases and accuracy improvements
- Implement and optimize hybrid search approaches combining semantic and keyword-based retrieval
- Evaluate and improve RAG system performance by using relevance metrics and A/B testing frameworks
- Design and implement advanced fine-tuning methods
- Create and manage synthetic data for fine-tuning
- Monitor and optimize fine-tuned model performance
- Manage a fine-tuned model from development through production deployment
- Last updated on 03/05/2026
- Purpose of this document
- About the exam
- Skills measured
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026