Design and implement an MLOps infrastructure
15–20% - Create Machine Learning workspaces, datastores, compute, data assets, and environments.
- Share assets with registries and deploy infrastructure with Bicep, Azure CLI, and GitHub Actions.
- Configure identity, network access, and source control for machine learning projects.
Practice MLOps Infrastructure Questions →
Implement machine learning model lifecycle and operations
25–30% - Orchestrate training with MLflow, automated ML, pipelines, and hyperparameter tuning.
- Register, version, evaluate, and archive models, including responsible AI checks.
- Deploy real-time and batch endpoints, then monitor drift and trigger retraining.
Practice Model Lifecycle & Operations Questions →
Design and implement a GenAIOps infrastructure
20–25% - Create Foundry projects with managed identities, RBAC, and private networking.
- Deploy foundation models, version them, and configure provisioned throughput.
- Version and compare prompts in Git.
Practice GenAIOps Infrastructure Questions →
Implement generative AI quality assurance and observability
10–15% - Build evaluation datasets and metrics for groundedness, relevance, coherence, and safety.
- Monitor latency, throughput, token cost, tracing, and logging in Foundry.
Practice GenAI Quality & Observability Questions →
Optimize generative AI systems and model performance
10–15% - Optimize RAG retrieval, embeddings, and hybrid search.
- Fine-tune models, manage synthetic data, and promote custom models to production.
Practice GenAI Optimization Questions →