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Following are some reference material for actual Cloudera CDP-3002 exam test
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CDP Platform Operations | 15% | - Data Lake and Storage - Cloudera Flow Management - Cloudera Data Platform Architecture - Cluster Management and Monitoring |
| Topic 2: Data Quality and Governance | 15% | - Data Catalog and Metadata - Data Lineage - Data Validation and Cleansing - Access Control and Security |
| Topic 3: Data Pipeline Orchestration | 20% | - Error Handling and Retries - Workflow Dependencies - Apache Airflow on CDP - Pipeline Scheduling and Triggers |
| Topic 4: Data Ingestion and Integration | 20% | - CDC (Change Data Capture) - Data Federation - Batch Data Ingestion - Stream Data Ingestion - Data Transformation and ETL |
| Topic 5: Data Processing with Spark | 30% | - Spark Core Concepts - DataFrame and Dataset APIs - Spark Structured Streaming - Spark Performance Optimization - Spark SQL and DataFrames |
Question 1
You're working with an Airflow DAG that performs data quality checks on sensitive dat a. How can you ensure data security during the checks?
A. All of the above
B. Utilize environment variables to store sensitive data and access them within the PythonOperator.
C. Store sensitive data like thresholds and comparison values directly within the DAG code.
D. Implement data masking techniques to obfuscate sensitive information during the checks.
Question 2
Which operator or feature in Apache Airflow can be used to dynamically adjust the schedule of data quality checks based on the volume of incoming data?
A. The Scheduler component with dynamic DAG generation
B. The BranchPythonOperator to choose between different scheduling paths
C. The ExternalTaskSensor to trigger data quality checks based on external events
D. A custom PythonOperator that modifies the DAG's schedule interval
Question 3
What is the difference between RDDs and DataFrames in Spark?
A. RDDs are mutable, while DataFrames are immutable
B. RDDs are lower-level abstractions, while DataFrames offer higher-level functionalities
C. Both B and C
D. RDDs represent distributed datasets, while DataFrames work with structured data
Question 4
You are working on a project that involves processing large datasets stored in HDFS. You need to read a CSV file into a DataFrame using PySpark. Which of the following code snippets correctly achieves this?
A.
B.
C.
D. 
Question 5
You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?
A. Schedule the DAG to run periodically, hoping the file becomes available eventually.
B. Configure the source system to notify Airflow when the file is ready for processing.
C. Implement a custom loop within a Python operator to continuously check for the file until it appears.
D. Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
Solutions:
| Question 1 Answer: B,D | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: D |
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