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Cloudera CDP Data Engineer - Certification Sample Questions:

1. An Airflow DAG is designed to ingest data from multiple sources, transform it, and load it into a data warehouse. The transformation step is resource-intensive and should not run during peak hours (9 AM to 5 PM). How can you configure the DAG to meet this requirement?

A) Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.
B) Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
C) Use the time_sensor operator to delay the transformation task until off-peak hours.
D) Set the max_active_runs parameter to limit executions during peak hours.

2. You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

A) Implement a custom function to write individual rows to the Hive table
B) Convert the DataFrame to a temporary table and then use HiveQL operations
C) Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
D) Use the DataFrame.write.saveAsTable("table_name") method

3. What is the purpose of partitioning data in Spark?

A) To enforce data access control
B) To optimize data visualization
C) To enable parallel processing across multiple nodes
D) To improve data compression efficiency

4. You're implementing a data quality process for Iceberg tables in CDP Which of the following Iceberg features can help you enforce constraints and detect data anomalies? (Choose two)

A) Manifest files
B) Metadata tables
C) Iceberg table constraints (not yet fully supporteD.
D) Partitioning
E) Snapshots

5. How does the Cloudera Optimization Framework leverage columnar storage formats for query performance improvement?

A) By encrypting data stored in columnar formats
B) By enabling more efficient compression and encoding schemes
C) By allowing more efficient data scans and operations on compressed data
D) By reducing network traffic through columnar data replication

Solutions: