Cloudera CCD-333 Exam Information and Actual Questions

  • Exam Code/Number: CCD-333
  • Exam Name/Title: Cloudera Certified Developer for Apache Hadoop
  • Certification Provider: Cloudera
  • Corresponding Certification: CCDH
  • Exam Questions: 60
  • Updated On: Aug 16, 2026

CCD-333
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Cloudera
CCD-333 Exam
Cloudera Certified Developer for Apache Hadoop

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Cloudera CCD-333 Exam Overview:

Certification Vendor:Cloudera
Exam Name:Cloudera Certified Developer for Apache Hadoop Exam (CCD-333)
Exam Number:CCD-333
Available Languages:English
Exam Price:$295 USD (varies by region and testing provider)
Related Certifications:Cloudera Certified Associate (CCA)
Cloudera Certified Administrator (CCA Administrator for Apache Hadoop)
Certificate Validity Period:2 years (typical Cloudera certification validity for legacy CCD exams)
Exam Format:Multiple Choice, Hands-on/Performance-based (varies by exam version and delivery format)
Passing Score:70%
Exam Duration:120 minutes
Real Exam Qty:Approximately 60
Recommended Training:Cloudera Training Courses
Exam Registration:Cloudera Certification Portal
Sample Questions:Cloudera CCD-333 Sample Questions
Exam Way:Online proctored or authorized test center (availability depends on region and provider)
Pre Condition:Recommended prior experience with Hadoop ecosystem and basic programming knowledge (Java or scripting languages).
Official Syllabus URL:https://www.cloudera.com/about/training/certification.html

Cloudera CCD-333 Exam Syllabus Topics:

SectionObjectives
Hadoop Ecosystem and Architecture- YARN Resource Management
  • 1. Cluster resource management concepts
    • 2. Resource allocation and scheduling
      - Hadoop Distributed File System (HDFS)
      • 1. HDFS architecture and components
        • 2. Data replication and fault tolerance
          Workflow and Scheduling- Oozie
          • 1. Workflow coordination and job scheduling
            Data Formats and Storage- Serialization formats
            • 1. Avro, Parquet, and SequenceFile concepts
              Data Ingestion and Integration- Sqoop
              • 1. Import/export between RDBMS and Hadoop
                - Flume
                • 1. Log collection and streaming ingestion
                  Data Processing with MapReduce- MapReduce programming model
                  • 1. Mapper and Reducer logic
                    • 2. Shuffle and sort phase
                      - Optimization and debugging
                      • 1. Job troubleshooting techniques
                        • 2. Performance tuning basics
                          Data Processing Tools- Pig
                          • 1. Pig Latin scripting
                            - Hive
                            • 1. SQL-like querying on Hadoop


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