Cloudera Machine Learning Engineer study guide
Cloudera Machine Learning Engineer · Work through your exam, one topic at a time.
The Cloudera Certified Associate (CCA) Machine Learning Engineer credential is intended for data scientists and ML engineers who build, deploy, and operate models on Cloudera's data and AI platform. This independent study page is designed for candidates searching for the exam: it organizes practice around five practical areas - workbench and project setup, data access and preparation, model development and experimentation, model deployment and serving in Cloudera AI, and the operations, monitoring, and governance of ML jobs. The Cloudera Machine Learning Engineer study guide has 5 sections and about 1,938 words.
01Machine Learning Workbench and Project Setup
This domain checks whether you can stand up a Cloudera AI Workbench project that is reproducible, secure, and shareable. Expect questions on project structure, sessions, runtimes and engines, resource profiles, Git-backed code, environment variables, secrets, and collaborator roles.
419 words, about 2 min02Data Access, Ingestion, and Preparation
This domain covers getting data from CDP storage and external systems into a form models can use. Questions target HDFS, Ozone, S3, ADLS, Hive and Impala tables, the Hive Metastore, Iceberg, JDBC sources, file formats, schemas, partitioning, and Spark-based preparation.
397 words, about 2 min03Model Development and Experimentation
This domain assesses reproducible model development in Cloudera AI: experiment tracking, parameter and metric logging, artifact capture, model comparison, and registration. Expect questions on experiment runs, random seeds, train, validation, and test splits, cross-validation, leakage, and the handoff from a notebook to a registered model version.
367 words, about 2 min04Model Deployment and Serving (Cloudera AI)
This domain covers turning a registered model into a governed inference endpoint in Cloudera AI. Expect questions on model versions, deployment configuration, replicas, resource profiles, autoscaling, request and response formats, authentication, rollout and rollback, and the difference between batch scoring and real-time serving.
379 words, about 2 min05Operations, Monitoring, and Governance of ML Jobs
This domain covers running ML workloads reliably in production: scheduled jobs, job runs, logs, retries, alerts, quotas, cost, model and data monitoring, audit, lineage, and access governance. Expect questions on idempotency, failure handling, drift detection, and evidence for compliance.
376 words, about 2 min