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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Orchestration | 20% | - Workflow Dependencies - Pipeline Scheduling and Triggers - Error Handling and Retries - Apache Airflow on CDP |
| CDP Platform Operations | 15% | - Cloudera Flow Management - Cloudera Data Platform Architecture - Cluster Management and Monitoring - Data Lake and Storage |
| Data Processing with Spark | 30% | - Spark SQL and DataFrames - DataFrame and Dataset APIs - Spark Performance Optimization - Spark Core Concepts - Spark Structured Streaming |
| Data Quality and Governance | 15% | - Data Lineage - Data Validation and Cleansing - Data Catalog and Metadata - Access Control and Security |
| Data Ingestion and Integration | 20% | - Batch Data Ingestion - Data Transformation and ETL - Stream Data Ingestion - CDC (Change Data Capture) - Data Federation |
Cloudera CDP Data Engineer - Certification Sample Questions:
Question 1
What is the impact of query vectorization in Cloudera's Optimization Framework?
A. It slows down query execution by adding complexity
B. It encrypts query results for security
C. It enables the execution of SQL commands
D. It improves query performance by processing batches of rows together
Question 2
A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?
A. Convert the DataFrame to an RDD and then back to a DataFrame.
B. Employ to redistribute data based on a key.
C. Use 'df.collect()'.
D. Apply 'df.coalesce(Y to reduce the number of partitions.
Question 3
Which Airflow operator is best used for executing a Python function as part of a DAG?
A. BashOperator
B. Dockeroperator
C. SimpleHttpOperator
D. PythonOperator
Question 4
In a PySpark application running on Kubernetes, if one of the Executors fails to execute a task due to a node failure, what action does the Spark Driver take?
A. It immediately shuts down the entire application.
B. It requests Kubernetes to allocate more resources to the failed Executor.
C. It pauses the application until the failed Executor is back online.
D. It reschedules the task on a different Executor.
Question 5
What impact does the Spark configuration parameter spark.network.timeout have on Spark streaming applications?
A. It determines the timeout for connections between executors and the Spark driver, impacting task retries and executor loss detection.
B. It controls the maximum time a Spark streaming application will wait for data before timing out.
C. It sets the timeout for Akka-based network operations, crucial for Spark streaming's stability.
D. It specifies the timeout for network operations such as data shuffle and broadcast.
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: A |
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