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Cloudera CDP Data Engineer - Certification Sample Questions:

1. How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

A) By directly writing SQL queries against distributed data in Spark.
B) By implementing custom logic in RDDs for data manipulation.
C) By using Spark ML libraries for machine learning tasks on data.
D) Both A and B.

2. You're building an Airflow DAG that involves multiple data processing tasks. How can you handle task dependencies and ensure the tasks execute in the correct order?

A) Use the Airflow UI to visually connect the tasks with arrows, indicating their execution order.
B) All of the above
C) Leverage XCom to share data between tasks and define dependencies based on the data availability.
D) Define the tasks directly within the DAG code using Python operators and set their dependencies manually.

3. Considering Hive's optimization mechanisms, under which scenario might partition pruning fail to improve query performance?

A) When querying data using a non-partition column as a filter
B) When querying data using the exact partition key in the WHERE clause
C) When the table is partitioned on a column frequently used in query filters
D) When the partitioned table contains a small number of partitions

4. Your Spark application on Kubernetes requires a secure connection to a database. You've stored the database password in Kubernetes Secrets. How would you typically access this secret in your PySpark application?

A) By using a ConfigMap
B) By querying the Kubernetes API
C) Through an external secrets manager
D) By reading the secret file from a volume mount

5. In Apache Airflow, how does the "catchup" parameter in a DAG definition influence the scheduling of DAG runs when backfilling data?

A) It limits the number of backfilled DAG runs to the most recent execution interval only.
B) It forces Airflow to ignore the start_date and schedule the DAG run immediately.
C) It enables backfilling, causing Airflow to schedule DAG runs for all intervals between the DAG's start_date and the current date.
D) It disables backfilling entirely, ensuring that only future DAG runs are executed.

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