PDF Version DemoCloudera CDP Data Engineer - Certification Sample Questions:
1. Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
A) They are automatically cleaned up after each task.
B) They reduce network I/O by making data available locally on each node.
C) They reduce the reliability of Spark applications.
D) They increase the amount of data shuffle during joins.
2. You're tasked with optimizing an existing Airflow DAG that processes large datasets daily. The DAG has multiple tasks, some of which frequently fail due to memory constraints on the worker nodes. Which approach would best mitigate this issue without upgrading hardware?
A) Use the Pool feature to limit the number of concurrent memory-intensive tasks.
B) Implement the Retry operator to automatically retry failed tasks.
C) Increase the priority_weight of memory-intensive tasks to ensure they run first.
D) Split the DAG into multiple smaller DAGs to reduce the load on workers.
3. How can you utilize Spark's lineage tracking feature to improve the efficiency of your data pipelines?
A) Leverage lineage information to identify and recompute only the affected data after failures
B) Manually track the lineage of each transformation for debugging purposes
C) Spark's lineage tracking doesn't offer any performance benefits
D) Use lineage tracking to optimize the order of transformations for better performance
4. In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?
A) To list the environment variables needed for the application.
B) To specify the Python version required for the application.
C) To list all the third-party dependencies required by the application.
D) To define the Spark version compatible with the application.
5. Which of the following strategies would NOT be recommended for managing skewed data during join operations in Spark?
A) Salting the keys to distribute the data more evenly across partitions.
B) Applying a filter to remove outliers that cause data skewness before joining.
C) Increasing the number of partitions to distribute the skewed data more evenly.
D) Using a broadcast join assuming one dataset is small enough to fit into memory.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |
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