Free Download Cloudera Data Engineer Certification Exam (CDP-DE)
Published 9/2025
Created by HadoopExam Learning Resources
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 72 Lectures ( 7h 3m ) | Size: 2.8 GB
Data Engineer exam topics with real interview-style Q&A-covering Spark on Kubernetes, Apache Airflow, Iceberg, Performan
What you'll learn
Design reliable data models on Cloudera Data Platform (CDP) using Apache Iceberg with ACID, time-travel, and schema evolution.
Build and optimize Apache Spark pipelines (DataFrame/Spark SQL) on Kubernetes, with correct executor sizing and shuffle strategy.
Implement incremental ETL/CDC patterns and idempotent upserts using Spark MERGE, checkpoints, and watermarking.
Tune Spark jobs for performance: fix skewed joins, enable AQE, prune partitions/columns, and reduce small files via compaction.
Choose effective partitioning, bucketing, and file-size targets for large fact tables to balance cost and speed.
Orchestrate pipelines with Apache Airflow: production-grade DAG design, retries/SLAs, alerts, and pre/post-load data quality checks.
Secure and operate pipelines on CDP with least-privilege access, secrets management, monitoring dashboards, and auditability.
Deploy and promote jobs via the CDP Data Engineering Service (DES) CLI/API, including blue/green and canary releases.
Diagnose failures quickly from Spark UI and Airflow logs; run safe rollbacks and targeted backfills.
Apply real exam patterns for the CDP Data Engineer certification: topic weightings, common traps, and time-saving strategies.
Map business queries to efficient table formats (Parquet vs Iceberg) and choose the right catalog/integration approach.
Measure success with concrete KPIs: wall-clock time, shuffle MB, task p95, data freshness, and DQ pass rates.
Requirements
No strict prerequisites - this course is interview-style and exam-focused. You can join as a motivated beginner.
Basic knowledge of SQL (SELECT, JOIN, GROUP BY) and data warehousing terms. (optional)
Familiarity with Apache Spark concepts (DataFrames, transformations vs. actions). (optional)
High-level understanding of Apache Airflow (DAGs, retries, alerts) and CI/CD ideas. (optional)
Comfort reading technical artifacts like Spark UI or Airflow logs. (optional)
Description
This course contains the use of artificial intelligence. Include 2 Practice Paper (120 Questions)Each Session pdf is available to download. Entire syllabus is covered for Cloudera CDP Data Engineer Certification exam, which is a main focus of this course. Are you preparing for the Cloudera Data Engineer Certification or aiming to succeed in data engineering job interviews? This course is designed to help you master Cloudera Data Platform (CDP) with a unique interview-style approach that goes far beyond theory. Instead of long labs or heavy coding exercises, you will learn through realistic conversations, scenario-based Q&A, and exam-focused explanations-the way hiring managers and certification exams truly test your knowledge.The Cloudera Data Engineer exam is one of the most recognized certifications for professionals in big data and cloud-based analytics. It validates your ability to design, develop, and optimize data workflows using Cloudera tools. The exam tests you on Apache Spark on Kubernetes, Apache Airflow, Apache Iceberg, performance tuning, deployment strategies, and CDP operations. With 50 questions in 90 minutes and a pass score of 55%, the exam demands both clarity of concepts and the ability to apply them under pressure.In this course, you'll cover all major domains:Spark (48%) - DataFrames, distributed processing, Hive integration, persistence, Spark SQL optimization, shuffle management, and Spark on Kubernetes.Airflow (10%) - Incremental extraction, ETL orchestration, DAG design, retries, SLAs, and quality checks.Performance Tuning (22%) - Explain plans, skew handling, broadcast joins, partitioning, bucketing, caching, and small-file remediation.Deployment (10%) - Using CDP Data Engineering Service (DES) CLI/API, monitoring jobs, canary releases, and rollbacks.Iceberg (10%) - Schema evolution, time travel, upserts/deletes, compaction, and hidden partitioning.By the end, you'll be able to confidently explain key concepts in an interview, tackle certification questions with precision, and apply best practices in real-world CDP projects. Whether you're a beginner to Cloudera or an experienced data engineer looking to validate your expertise, this course gives you a clear, structured, and exam-oriented path to success.Join today and get one step closer to becoming a Cloudera-certified Data Engineer and standing out in the competitive world of big data.
Who this course is for
Data Engineers & Big Data Developers who work (or plan to work) on the Cloudera Data Platform (CDP) and want a fast, interview-style path to mastery with Apache Spark, Airflow, and Iceberg.
Spark Developers / Analytics Engineers moving from ad-hoc jobs to production-grade ETL/CDC, looking to sharpen data modeling, partitioning, and performance tuning on Kubernetes.
Platform / DataOps / MLOps Engineers responsible for deploying and operating data pipelines on CDP, including DES CLI/API, observability, SLAs, and least-privilege security.
Cloud Data Engineers (AWS/Azure/GCP) who use Spark today and want to understand CDP concepts and how they map to real exam and interview expectations.
Certification Candidates preparing for the Cloudera Data Engineer exam who prefer scenario-based Q&A over hands-on labs and want clarity on topic weightings, common traps, and time-saving strategies.
Experienced SQL/ETL Professionals transitioning from on-prem or Hadoop/Hive to modern table formats (Iceberg) and orchestrators (Airflow) without needing a full lab environment.
Team Leads / Hiring Managers seeking a structured framework to evaluate candidates' readiness across Spark fundamentals, Airflow orchestration, performance tuning, and CDP deployment.
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