Model Deployment — Databricks Certified Machine Learning Associate

The official DATABRICKS documentation our Model Deployment practice questions are cited to. Review the primary sources, then practise.

Official references for this objective

Databricks — Build and Deploy Databricks Projects Using Automation Bundles and Genie ZeroOps | Databricks

DABs let teams declare jobs, pipelines, tests, environments, and dependencies as code. That representation enables Git workflows, modularity, branching, repeatable multi-environment deployments
Databricks — Running LLaMA at Scale: Production Inference on Databricks Model Serving | Databricks

We’ll cover validation checks—A/B tests, golden sets—plus the cost model we used to compare against our internal vLLM-based stack, including levers like dynamic batching, autoscaling
Databricks — High-Throughput, Low-Latency: The Databricks Playbook for Production Model Serving | Databricks

learn how to design your endpoints for bursty and always-on traffic, and leave with practical guidance for running mission-critical ML and LLM workloads on Databricks
Databricks — Unlocking Video Data at Scale: VLM Batch Inference with Ray on Databricks | Databricks

(1) video ingestion into Unity Catalog Volumes, (2) VLM registration with MLflow for reproducibility, and (3) distributed batch inference using Ray and VLLM with Qwen2.5-VL-32B