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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're given a DataFrame containing information about flights, including columns "origin", "destination", and "delay_minutes". How can you find the top 5 origin airports with the most delayed flights on average?

A) Leverage Spark SQL's RANK function along with windowing to identify top 5 origins
B) Implement a custom function to calculate average delays for each origin and then sort and filter
C) Use Spark's machine learning library (MLIiB. for ranking and classification
D) Use groupBy and avg on "delay_minutes", then sort by the average in descending order and limit to top 5

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

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

3. 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.

4. In Apache Airflow, how can you dynamically generate tasks for each table in your database that needs a quality check?

A) Use the Variable feature to store a list of tables and iterate over them with a PythonOperator.
B) Implement a BranchPythonOperator to create branches for each table dynamically.
C) Use the SubDagOperator to create a sub-DAG for each table.
D) Utilize the Dynamic Task Mapping feature to create a task for each table.

5. You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?

A) Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
B) Schedule each pipeline separately with appropriate scheduling intervals.
C) Implement a custom script to manage the execution order of the pipelines.
D) Run all pipelines simultaneously, assuming they are independent.

Solutions: