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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| CDP Platform Operations | 15% | - Data Lake and Storage - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management |
| Data Quality and Governance | 15% | - Access Control and Security - Data Validation and Cleansing - Data Catalog and Metadata - Data Lineage |
| Data Ingestion and Integration | 20% | - CDC (Change Data Capture) - Stream Data Ingestion - Data Transformation and ETL - Data Federation - Batch Data Ingestion |
| 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% | - Pipeline Scheduling and Triggers - Apache Airflow on CDP - Error Handling and Retries - Workflow Dependencies |
Cloudera CDP Data Engineer - Certification Sample Questions:
Question 1
Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?
A. Completely disabling schema inference and relying on static schemas
B. Manually updating the inferred schema for each data ingestion
C. Integrating schema registry services that track and manage schema versions
D. Using a single version of the schema for all data, regardless of changes
Question 2
What is the primary purpose of using Cloudera's API in a data engineering context?
A. To automate the provisioning, configuration, and scaling of clusters.
B. To manually configure and start cluster services.
C. To increase the time it takes to deploy applications.
D. To bypass security configurations for rapid development.
Question 3
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. Increase the number of executors in your cluster
B. Reduce the number of partitions in your RDDs
C. All of the above
D. Use a broadcast variable for small, frequently accessed data
Question 4
When monitoring a PySpark application in Kubernetes, you notice that Executor pods are frequently restarting. What is the most likely cause of this issue?
A. Insufficient memory allocation for Executor pods.
B. Executor pods are not configured to restart upon failure.
C. Too many tasks assigned to each Executor.
D. Dynamic resource allocation is disabled.
Question 5
You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?
A. Broadcast the skewed data to all executors.
B. Addressing skewed data requires
C. Use salting on the skewed column during data partitioning.
D. Implement custom partitioners to evenly distribute skewed values.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: A | Question 5 Answer: B |
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