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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Pipeline Scheduling and Triggers - Apache Airflow on CDP - Error Handling and Retries |
| Data Ingestion and Integration | 20% | - Data Transformation and ETL - Batch Data Ingestion - Stream Data Ingestion - Data Federation - CDC (Change Data Capture) |
| CDP Platform Operations | 15% | - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage |
| Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Catalog and Metadata - Access Control and Security - Data Lineage |
| Data Processing with Spark | 30% | - Spark Core Concepts - Spark SQL and DataFrames - DataFrame and Dataset APIs - Spark Structured Streaming - Spark Performance Optimization |
Cloudera CDP Data Engineer - Certification Sample Questions:
Question #1
You're tasked with monitoring the performance and resource utilization of your Spark jobs. What tools and techniques can you employ for effective monitoring and troubleshooting?
A. Use Spark's web UI for basic job status information
B. Manually analyze Spark logs after job completion
C. Leverage YARN resource manager and Spark metrics for detailed monitoring
D. Implement custom instrumentation code within your Spark application
Question #2
What is the significance of "Sort Merge Join" appearing in an Explain Plan in Cloudera's SQL engines?
A. It suggests that the join operation is performed without sorting, leading to faster execution
B. It indicates that the query will benefit from additional indexes
C. It signifies that the join operation is performed by sorting and then merging two datasets, which can be efficient for large, sorted datasets
D. It is the least preferred join method due to its high CPU usage
Question #3
What is the purpose of partitioning data in Spark?
A. To enable parallel processing across multiple nodes
B. To improve data compression efficiency
C. To enforce data access control
D. To optimize data visualization
Question #4
You're tasked with scheduling an ETL pipeline in Airflow that extracts data from a database, transforms it, and loads it into a data warehouse. How can you achieve this using Airflow operators?
A. Implement a single custom Python script containing the entire ETL logic and call it within a BashOperator.
B. Utilize Airflow's built-in ETL operators like BigQueryOperator or MySqlOperator (these operators are specific to certain data sources and not generally applicable for all ETL pipelines).
C. Use three separate Python operators for each stage (extract, transform, loaD. and chain them together in the DAG.
D. Leverage dedicated operators like PostgresHook for extraction, custom Python operators for transformation, and S3Hook for loading.
Question #5
Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?
A. Airflow Plugins
B. Variables
C. XComs
D. Jinja Templating
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: C | Question #3 Correct Answer: A | Question #4 Correct Answer: D | Question #5 Correct Answer: D |
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