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Cloudera CDP Data Engineer - Certification Sample Questions:

1. When configuring a Hive table with bucketing, how does the choice of the bucketing column(s) influence query performance?

A) Selecting highly unique or skewed columns for bucketing can lead to uneven data distribution and potential performance bottlenecks.
B) The choice of bucketing column(s) has no impact on performance as long as the number of buckets is optimally chosen.
C) Bucketing on columns that are frequently updated optimizes data modification operations and query performance.
D) The bucketing column(s) should be chosen randomly to ensure a uniform distribution of data across buckets.

2. Which Apache Airflow feature should be used to parameterize a DAG run for running data quality checks on different datasets dynamically?

A) Custom Execution Context
B) Jinja templating
C) XComs
D) Airflow Variables

3. In an Airflow DAG, you have tasks A, B, C, and D. Task A must complete before B and C can start, but B and C can run in parallel. Task D should only run once both B and C have completed. How do you set up these dependencies?

A) A]] B, C]] D
B) [A ]] B, A]] C] and [B, C] ]] D
C) A.set_downstream([B, C]) and set_upstream([B, C])
D) A]] B ]] C]] D

4. Which of the following is TRUE about using Explain Plans for performance tuning?

A) They are only useful for queries that involve joins
B) Explain Plans always show the actual runtime of a query
C) They provide detailed network latency information
D) They can help identify operations that may become performance bottlenecks

5. You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

A) Implement exception handling within your custom Python operators and define retry behavior manually.
B) All of the above
C) Configure the DAG to retry failed tasks automatically with a set number of retries.
D) Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.

Solutions: