Free Practice Questions for Microsoft Azure AI Engineer Associate (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