Cloudera CDP Machine Learning Engineer

Exam facts (full table)

Vendor source — Cloudera CDP Machine Learning Engineer ↗

About

The CDP Machine Learning Engineer certification validates professional expertise in developing, deploying, and managing machine learning workflows on the Cloudera Data Platform. This role-specific certification is designed for data scientists, ML engineers, and analytics professionals implementing production ML systems at scale.

Certified ML engineers demonstrate proficiency in model development, experiment tracking, feature engineering, model serving, monitoring, and integration with CDP's data engineering and governance frameworks—essential skills for building enterprise machine learning solutions.

Domain context

Machine learning on CDP requires bridging data science and engineering disciplines:

Model Development: Building ML models using popular frameworks (Scikit-learn, TensorFlow, PyTorch) on distributed data. Leveraging Spark MLlib for scalable machine learning. Understanding model selection, hyperparameter tuning, and validation strategies.

Experiment Tracking & Reproducibility: Using MLflow for tracking experiments, managing model versions, and ensuring reproducibility. Recording parameters, metrics, and artifacts for model governance.

Feature Engineering: Building efficient feature pipelines on Spark. Using Hive/Iceberg for feature storage. Implementing feature transformations at scale and managing feature lifecycle.

Model Serving & Inference: Deploying models for batch and real-time inference. Integrating with CDP Data Services. Managing model endpoints and scaling inference workloads.

Model Monitoring & Governance: Monitoring model performance in production. Detecting data drift and model degradation. Implementing governance controls with Ranger. Managing model lineage and reproducibility.

Integration with Data Platform: Leveraging CDP's data catalog, security, and governance. Integrating with data engineering pipelines. Using CDP services for end-to-end ML workflows.

Topics covered

Machine Learning Fundamentals (20% of exam weight)

• Supervised vs. unsupervised learning
• Regression and classification algorithms
• Model evaluation metrics and validation
• Overfitting and regularization
• Cross-validation and hyperparameter tuning
• Feature scaling and normalization
• Imbalanced classification handling

Feature Engineering (15% of exam weight)

• Feature selection and importance analysis
• Feature transformation and encoding
• Feature scaling and standardization
• Handling missing values and outliers
• Feature interaction and polynomial features
• Time-series feature engineering
• Categorical and numerical feature engineering

Spark MLlib and Distributed ML (25% of exam weight)

• Spark MLlib architecture and algorithms
• RDD-based vs. ML Pipeline API
• Decision trees and ensemble methods
• Clustering algorithms (K-means, Gaussian mixture)
• Dimensionality reduction (PCA)
• Recommendation systems
• Parameter tuning on distributed data
• Model persistence and serialization

Experiment Tracking with MLflow (15% of exam weight)

• MLflow architecture and components
• Experiment tracking and runs
• Logging parameters, metrics, and artifacts
• Model registry and versioning
• MLflow projects and reproducibility
• Hyperparameter search integration
• Model serving with MLflow Models

Model Serving and Deployment (15% of exam weight)

• Batch prediction workflows
• Real-time model serving
• Model endpoints and APIs
• Scaling inference workloads
• Model A/B testing and canary deployments
• Monitoring model predictions
• Integration with CDP Data Services

Monitoring and Governance (10% of exam weight)

• Model performance monitoring
• Data drift and model degradation detection
• Model explainability and interpretability
• Ranger policy integration with models
• Model lineage and audit trails
• Data quality checks in ML pipelines
• Compliance and ethical AI considerations

Common job-ready skills

• Develop ML models using Spark MLlib and scikit-learn
• Engineer features at scale for ML pipelines
• Design and implement experiment tracking with MLflow
• Deploy and serve models for batch and real-time inference
• Monitor model performance and detect degradation
• Integrate ML workflows with CDP data platform
• Implement security and governance for ML models
• Build reproducible and auditable ML systems
• Optimize ML pipeline performance on distributed systems
• Troubleshoot and debug ML workflows

Recommended courses

• Cloudera University: CDP Machine Learning Engineer Official Training
• Udemy: Machine Learning on Spark and CDP courses
• Coursera: Machine Learning Specialization (Andrew Ng)
• DataCamp: Spark MLlib and Advanced ML
• Pluralsight: Advanced Machine Learning with Spark
• Fast.ai: Deep Learning and Practical Deep Learning

Practice exams

• AnalyticsExam: CDP-6001 practice tests
• Udemy: CDP Machine Learning practice exams
• QuickTechie: CDP-6001 Q&A and practice questions
• MLflow documentation and tutorials

Books

• "Machine Learning with Spark" by Nick Pentreath
• "Spark: The Definitive Guide" by Bill Chambers & Matei Zaharia
• "MLflow in Action" by Gabriel Morin
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
• "Feature Engineering for Machine Learning" by Alice Zheng

Job titles

• Machine Learning Engineer
• ML Engineer – Platform
• Data Scientist
• Senior Data Scientist
• ML Ops Engineer
• Analytics Engineer
• Data Science Engineer
• Feature Engineering Specialist
• AI/ML Solutions Architect
• ML Platform Engineer

Salary (USD / ZAR×18 / GBP / EUR / AUD)

Note: ML engineer roles typically command 20-50% premium over general data engineer roles. Combined certifications (Data Engineer + ML) can command additional 15-25% premium.

Skills validated

• Machine learning algorithms and theory
• Distributed ML with Spark MLlib
• Feature engineering at scale
• Experiment tracking and reproducibility
• Model development and validation
• Model serving and inference optimization
• Monitoring and observability for ML
• Governance and compliance for ML systems
• CDP data platform integration
• ML pipeline orchestration

Related certs

• Cloudera CDP-0011: Generalist (foundational, complementary)
• Cloudera CDP-3002: Data Engineer (complementary pipeline skills)
• Cloudera CDP-5001: Administrator – Public Cloud (infrastructure)
• Databricks Machine Learning: Alternative ML certification
• Google Cloud Professional Data Engineer: Cloud ML focus

Sources

• Cloudera: CDP Machine Learning Engineer (CDP-6001)
• CertificationBox: CDP-6001 Roadmap
• Cloudera: Certification FAQ
• MLflow: Official Documentation
• Spark MLlib: Official Documentation