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Cloudera Data Engineer — Requirements & Free Practice

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Cloudera Data Engineer

Role-based Cloudera platform data engineering certification earned through the Cloudera Data Engineer examination and awarded with a digital badge; individual certifications are valid for two years.

Licensing authority: Cloudera · Application information

Initial by examination
Cloudera Data Engineer
Award
Pass the role-specific question-based examination; the current published pass score is 55%.
Certification Validity
Two years from the individual's certification; this is not an examination retirement date.
Delivery
Online proctored only; no allowed reference resources. Identity documentation, webcam monitoring and an otherwise empty, quiet room are required by the published exam rules.
Evidence Claim Ids
["cloudera-de-registration","cloudera-de-assessment","cloudera-de-credential-authority"]
Preparation
Suggested training is explicitly optional; real-world hands-on experience is highly recommended. The audience describes professional knowledge expectations rather than a separate training credential or prerequisite exam.
Registration
Purchase through Cloudera Education, then schedule a date and time in Questionmark; daily time slots are limited and requested future dates are not guaranteed.
Rescheduling
Cancel the existing reservation before rescheduling, up to 24 hours before the start.
Technical Preparation
The FAQ requests Zoom installation, system and communication/bandwidth tests, and review of the exam process 48 hours before the appointment if possible.

Cloudera Data Engineer

Provider
Questionmark
Format
Question-based, online proctored; no reference resources allowed.
Exam questions
Total questions: 50.
Time limit
90 minutes
Passing score
55%
Exam fee
$330.00 USD per attempt
Examination outline
  • Spark (48%)
    • Fundamentals on Spark over Kubernetes
    • Work with DataFrames
    • Understand Distribute Processing
    • Implement Hive and Spark Integration
    • Understand Distributed Persistence
  • Airflow (10%)
    • Implement incremental extraction in Apache Airflow from source system
    • Use Apache Airflow to schedule ETL pipelines
    • Use Apache Airflow to schedule quality checks
    • Work with DAGs
  • Performance Tuning (22%)
    • Know Basic tools in (Spark) Performance Tuning
    • Understand Optimization Framework and Explain plans
    • Understand Inferring Schemas
    • Join performance and caching data for reuse
    • Work with Partitioned and Bucketed Tables
  • Deployment (10%)
    • Use the API and CLI
    • Work in the Data Engineering Service
  • Iceberg (10%)
    • Understand Iceberg
Examination sources