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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| CDP Platform Operations | 15% | - Cloudera Flow Management - Cloudera Data Platform Architecture - Data Lake and Storage - Cluster Management and Monitoring |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Pipeline Scheduling and Triggers - Error Handling and Retries - Apache Airflow on CDP |
| Data Processing with Spark | 30% | - Spark SQL and DataFrames - Spark Performance Optimization - Spark Core Concepts - DataFrame and Dataset APIs - Spark Structured Streaming |
| Data Quality and Governance | 15% | - Access Control and Security - Data Catalog and Metadata - Data Validation and Cleansing - Data Lineage |
| Data Ingestion and Integration | 20% | - Data Transformation and ETL - CDC (Change Data Capture) - Stream Data Ingestion - Data Federation - Batch Data Ingestion |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. In Apache Airflow, how can you dynamically generate tasks for each table in your database that needs a quality check?
A) Use the Variable feature to store a list of tables and iterate over them with a PythonOperator.
B) Implement a BranchPythonOperator to create branches for each table dynamically.
C) Use the SubDagOperator to create a sub-DAG for each table.
D) Utilize the Dynamic Task Mapping feature to create a task for each table.
2. You encounter an error message stating "Task timed out" while running your Airflow DAG. What are some potential causes and how can you troubleshoot them?
A) All of the above
B) The data processing task is taking longer than the configured timeout for the task.
C) The DAG is running too frequently, overloading the system resources.
D) The extraction task is encountering issues connecting to the source system.
3. What is the purpose of partitioning data in Spark?
A) To optimize data visualization
B) To improve data compression efficiency
C) To enforce data access control
D) To enable parallel processing across multiple nodes
4. When would it be advantageous to use both partitioning and bucketing on a Hive table?
A) When data security is a primary concern
B) When data needs to be stored in a single file for archival purposes
C) When managing small datasets to reduce complexity
D) When dealing with large datasets that require efficient querying and data sampling
5. In Apache Airflow, how does the "catchup" parameter in a DAG definition influence the scheduling of DAG runs when backfilling data?
A) It forces Airflow to ignore the start_date and schedule the DAG run immediately.
B) It disables backfilling entirely, ensuring that only future DAG runs are executed.
C) It enables backfilling, causing Airflow to schedule DAG runs for all intervals between the DAG's start_date and the current date.
D) It limits the number of backfilled DAG runs to the most recent execution interval only.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |







