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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a Spark application that processes sensitive dat
a. How can you ensure that persisted data remains secure even if accessed from unauthorized sources?

A) Implement custom access control mechanisms within your application
B) Rely on Spark's lineage tracking to prevent unauthorized access
C) No additional security measures are needed, as Spark handles data security
D) Encrypt the data before persisting it and decrypt it when needed

2. You need to read data from a Hive table into a Spark DataFrame. Which approach would be the most efficient?

A) Convert the Hive table to a managed table and then use spark.read.table("table_name")
B) Use the spark.read.parquet("/path/to/hive/table") method directly
C) Leverage Spark SQL capabilities with SELECT FROM table_name
D) Use the FROM table_name") method

3. You encounter an error message stating "Schema mismatch" when joining two DataFrames in Spark. What could be the potential causes and how can you resolve them?

A) The DataFrames have different column names but the same data types
B) The DataFrames have the same column names but different data types
C) The join condition references non-existent columns in one of the DataFrames
D) All of the above

4. Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?

A) Variables
B) Jinja Templating
C) XComs
D) Airflow Plugins

5. You're building a complex Airflow DAG with numerous tasks and dependencies. How can you improve the DAG's readability and maintainability?

A) Break down the DAG into smaller sub-DAGs with well-defined functionalities.
B) Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.
C) All of the above
D) Implement extensive logging within each task to capture detailed execution information.

Solutions: