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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Spark application involves a complex data pipeline with multiple dependent stages. How can you configure Spark to handle failures gracefully and ensure data consistency across the pipeline?

A) Leverage checkpointing and lineage tracking for selective failure recovery
B) Retry failed stages indefinitely until successful completion
C) Use Spark's built-in fault tolerance mechanisms with automatic retries
D) Implement custom error handling logic within each stage

2. What mechanism does Apache Airflow provide to delay the execution of a task until a certain condition is met?
A The delay parameter in task definitions.

A) The execution_timeout attribute, which can be set to postpone task execution.
B) Sensors, which are special operators that wait for a certain condition to be true.
C) The wait_for_downstream setting in the DAG configuration.

3. You encounter an error during the execution of your Airflow DAG. How can you identify the root cause of the issue and debug it effectively?

A) Inspect the code of failed tasks directly within the Airflow web Ul for potential issues.
B) Utilize Airflow logs (scheduler, worker) to analyze detailed error messages and stack traces.
C) Rely on the error message displayed on the Airflow UI, even if it's generic.
D) All of the above

4. A colleague proposes using Iceberg's optimistic locking feature for a high-concurrency scenario where multiple jobs frequently modify the same table. What potential issues should you raise?

A) Optimistic locking might result in many failed transactions and retries, potentially leading to job instability.
B) Optimistic locking in Iceberg introduces a significant performance overhead.
C) Iceberg's optimistic locking isn't suitable for distributed environments like CDP.
D) Iceberg's optimistic locking requires manual conflict resolution.

5. For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

A) The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
B) Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
C) Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
D) Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.

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