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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Quality and Governance | 15% | - Data Validation and Cleansing - Access Control and Security - Data Catalog and Metadata - Data Lineage |
| CDP Platform Operations | 15% | - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage |
| Data Processing with Spark | 30% | - DataFrame and Dataset APIs - Spark Core Concepts - Spark Performance Optimization - Spark Structured Streaming - Spark SQL and DataFrames |
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Apache Airflow on CDP - Pipeline Scheduling and Triggers - Error Handling and Retries |
| Data Ingestion and Integration | 20% | - CDC (Change Data Capture) - Data Transformation and ETL - Data Federation - Batch Data Ingestion - Stream Data Ingestion |
Cloudera CDP Data Engineer - Certification Sample Questions:
Question 1
You need to filter data from a Hive table based on a specific date range. Which approach would be most efficient and maintainable?
A. Use Spark SQL functions like filter with a date comparison expression
B. Implement a custom filter function in Spark to process each row individually
C. Convert the Hive table to a temporary table and then use Spark SQL filtering
D. Leverage HiveQL's built-in filtering capabilities with a WHERE clause
Question 2
You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?
A. Use a broadcast variable for small, frequently accessed data
B. Reduce the number of partitions in your RDDs
C. Increase the number of executors in your cluster
D. All of the above
Question 3
Which Airflow operator is best used for executing a Python function as part of a DAG?
A. PythonOperator
B. Dockeroperator
C. SimpleHttpOperator
D. BashOperator
Question 4
Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?
A. Task Re-execution
B. Checkpointing
C. Data Replication
D. Master Node Redundancy
Question 5
Which of the following statements best describes the process of schema inference in the context of big data processing?
A. Automatically determining the data structure of a dataset.
B. Assigning random data types to columns in unstructured data.
C. Encrypting data based on its type.
D. Manually defining the schema for each dataset before processing.
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: A |
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