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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Due to regulatory requirements, you need to permanently delete specific sensitive records from an Iceberg table. Which of the following techniques would be most appropriate?

A) Modify the data files directly using low-level tools to overwrite the sensitive data.
B) Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.
C) Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.
D) Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.

2. You're building a large and complex ETL pipeline with numerous tasks and dependencies. What are some best practices to ensure its maintainability and understandability?

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

3. In the context of dynamic partitioning in Hive, what challenge does the use of too many dynamic partitions in a single load operation present?

A) It enhances data security by segmenting data into finer-grained partitions.
B) It can lead to an excessive number of small files, negatively impacting HDFS performance.
C) It simplifies the management of Hive metadata, reducing the load on the NameNode.
D) It automatically disables the execution of map-reduce jobs.

4. You need to design your Airflow DAG for data quality checks to be easily monitored and visualized. How can you achieve this?

A) Leverage Airflow's built-in metrics and monitoring features like the Airflow web UI to track check execution and results.
B) Utilize Airflow XCom to share the outcome of the checks (success/failurE. with downstream tasks for further processing.
C) Implement custom logic to send detailed logs about the check results to external monitoring tools.
D) All of the above

5. Your Airflow DAG performs data quality checks that involve complex data transformations and aggregations. How can you ensure these checks are executed efficiently and don't impact the performance of the entire pipeline?

A) Utilize libraries like Pandas or Spark for data manipulation within the PythonOperator, leveraging optimization features where available.
B) Use Airflow's built-in data manipulation operators like SQLExecuteOperator for basic checks, avoiding complex transformations.
C) Offload complex data processing tasks to separate DAGs designed specifically for data preparation.
D) Implement the data transformations and aggregations directly within the checking logic, regardless of efficiency.

Solutions: