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Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Related Certifications: | Cloudera Certified Associate (CCA) Data Analyst Cloudera Certified Professional (CCP) Data Engineer |
| Exam Format: | Multiple Choice, Hands-on Lab (Performance-based) |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Passing Score: | 70% |
| Exam Price: | USD 295 |
| Available Languages: | English |
| Sample Questions: | Cloudera CDP-3002 Sample Questions |
| Exam Way: | Online proctored exam at authorized testing centers |
| Pre Condition: | Recommended: Hands-on experience with Cloudera CDP, familiarity with Python/Scala, and understanding of distributed data processing concepts |
| Official Syllabus URL: | https://www.cloudera.com/about/certification/cdp-certification.html |
Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing with Spark | 30% | - Spark Core Concepts - Spark Performance Optimization - DataFrame and Dataset APIs - Spark SQL and DataFrames - Spark Structured Streaming |
| Topic 2: Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Lineage - Data Catalog and Metadata - Access Control and Security |
| Topic 3: Data Ingestion and Integration | 20% | - Stream Data Ingestion - CDC (Change Data Capture) - Batch Data Ingestion - Data Federation - Data Transformation and ETL |
| Topic 4: CDP Platform Operations | 15% | - Data Lake and Storage - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management |
| Topic 5: Data Pipeline Orchestration | 20% | - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP - Pipeline Scheduling and Triggers |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. Why are partitioned tables beneficial in Hive for large datasets?
A) They allow for faster query processing by scanning only relevant partitions.
B) They decrease the security of the data.
C) They ensure that all data is stored in a single file, simplifying access.
D) They automatically encrypt data based on the partition key.
2. In the context of Hive, what mechanism ensures that data is evenly distributed across buckets?
A) External data balancing tools
B) Manual data insertion scripts
C) A hash function applied to the bucketing column
D) Natural key distribution
3. How do bucketed tables in Hive improve query performance?
A) By organizing data into fixed-size segments, or buckets, based on a hash function of a column
B) By replicating data across multiple nodes to improve access speed
C) By compressing data automatically to reduce storage space
D) By encrypting data to reduce 1/0 operations
4. What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations
A) The serialization format of data
B) The compression codec used for shuffle files
C) The memory allocation for executor instances
5. Your Spark application involves complex data transformations requiring multiple shuffles. How can you leverage Spark's in- memory caching mechanisms to improve performance?
A Use rdd.persist(StorageLevel.MEMORY_ONLY) for all intermediate RDDs
A) Implement custom caching logic using in-memory data structures
B) Leverage Spark SQL caching with materialized views
C) Tune Spark configuration parameters like spark.storage.memoryFraction
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |
By Beatrice
