Snowflake SnowPro Advanced: MLOps Engineer (MLA-B01) Lessons
17 lessons, one per exam-guide subdomain, in the order the guide teaches them. Every claim is cited to the official documentation.
Domain 1: Operationalize Data Preparation and Feature Engineering
5 lessons · 20% of the exam
1.1Distributed Feature Engineering with Snowpark and Snowflake ML Preprocessors
Subdomain 1.1: Construct distributed feature engineering pipelines.
2 pages · 22 min read
1.2Snowflake Feature Store: Architecture, Entities and Feature Views
Subdomain 1.2: Implement Snowflake Feature Store architecture and management.
2 pages · 24 min read
1.3Point-in-Time Training Sets in Snowflake Feature Store
Subdomain 1.3: Ensure temporal integrity and feature consistency.
2 pages · 19 min read
1.4Snowflake Lineage, Snowpipe Streaming and Feature Store Monitoring
Subdomain 1.4: Configure automated ingestion and data quality.
2 pages · 23 min read
1.5Feature views as versioned assets: packaging and scaling feature transformations
Subdomain 1.5: Operationalize features as first-class data assets.
2 pages · 19 min read
Domain 2: MLOps Infrastructure and Management
3 lessons · 24% of the exam
2.1Compute Pools and Warehouses for Snowflake ML Workloads
Subdomain 2.1: Manage infrastructure for ML.
2 pages · 22 min read
2.2Notebooks in Workspaces: Notebook Services, GPU Allocation and When to Use Them
Subdomain 2.2: Utilize Snowflake Workspaces.
2 pages · 21 min read
2.3Snowflake ML Experiments: Logging and Comparing Training Runs
Subdomain 2.3: Track experiments and metadata.
2 pages · 24 min read
Domain 3: Model Serving and Deployment Operations
3 lessons · 18% of the exam
3.1Logging Models, Custom Models and Artifacts in the Snowflake Model Registry
Subdomain 3.1: Operate the Snowflake Model Registry.
2 pages · 27 min read
3.2Real-time model inference on SPCS: deploy, call and monitor REST endpoints
Subdomain 3.2: Implement inference deployment patterns.
2 pages · 25 min read
3.3Migrating Third-Party Models into the Snowflake Model Registry
Subdomain 3.3: Execute platform migrations.
2 pages · 22 min read
Domain 4: Pipeline Orchestration and Automation (CI/CD)
3 lessons · 22% of the exam
4.1Building an ML pipeline with modular code and Snowflake ML Jobs
Subdomain 4.1: Orchestrate end-to-end ML workflows.
2 pages · 22 min read
4.2Git Integration in Snowflake for ML Code Deployment
Subdomain 4.2: Configure CI/CD and version control.
2 pages · 19 min read
4.3Drift-Triggered Retraining Policies with Model Monitors, Alerts and Tasks
Subdomain 4.3: Implement retraining and troubleshooting.
2 pages · 23 min read
Domain 5: Governance, Security, and Monitoring
3 lessons · 16% of the exam
5.1Snowflake ML Privileges: Feature Store, Model Registry and Compute Pools
Subdomain 5.1: Enforce Snowflake security policies.
2 pages · 21 min read
5.2Snowflake ML Observability: model monitors for drift and accuracy
Subdomain 5.2: Monitor model health and compliance.
2 pages · 22 min read
5.3ML Cost Attribution in Snowflake: Object Tags, Query Tags and Feature View Costs
Subdomain 5.3: Manage ML cost attribution and resource optimization.
2 pages · 19 min read