Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions — Information & Free Practice

The authority publishes percentage ranges for each content area. The practice percentages below use the midpoint of each range, normalized to 100%; they are not exact percentages published by the authority. Practice question counts may differ slightly because of rounding.

• Design and implement an MLOps infrastructure — Official range: 15–20% (Practice allocation: 18.92%)

  • Create and manage resources in a Machine Learning workspace
  • Create and manage assets in a Machine Learning workspace
  • Implement IaC for Machine Learning
• Implement machine learning model lifecycle and operations — Official range: 25–30% (Practice allocation: 29.73%)

  • Orchestrate model training
  • Implement model registration and versioning
  • Deploy machine learning models for production environments
  • Monitor and maintain machine learning models in production
• Design and implement a GenAIOps infrastructure — Official range: 20–25% (Practice allocation: 24.32%)

  • Implement Foundry environments and platform configuration
  • Deploy and manage foundation models for production workloads
  • Implement prompt versioning and management with source control
• Implement generative AI quality assurance and observability — Official range: 10–15% (Practice allocation: 13.51%)

  • Configure evaluation and validation for generative AI applications and agents
  • Implement observability for generative AI applications and agents
• Optimize generative AI systems and model performance — Official range: 10–15% (Practice allocation: 13.51%)

  • Optimize retrieval-augmented generation (RAG) performance and accuracy
  • Implement advanced fine-tuning and model customization