Free Practice Questions for Microsoft Certified: Operationalizing Machine Learning and Generative AI Solutions (AI-300) Certification

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

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

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

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

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