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[General] Dumps CDP-3002 Free | Dumps CDP-3002 Torrent

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【General】 Dumps CDP-3002 Free | Dumps CDP-3002 Torrent

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q208-Q213):NEW QUESTION # 208
You're working with a large Spark DataFrame and need to perform an aggregation operation (e.g., can you improve the performance of the aggregation? SUM, COUNT). How
  • A. Increase the number of Spark executors without further optimization
  • B. Leverage partitioning techniques to group relevant data together
  • C. All of the above
  • D. Use Spark SQL's built-in aggregation functions like SUM and COUNT
Answer: C
Explanation:
While increasing executors A might help, it's not the sole solution. Using built-in functions B is essential. Partitioning the DataFrame C based on the column involved in the aggregation reduces data shuffling across the network, significantly improving performance, especially for large datasets.

NEW QUESTION # 209
You encounter an error during a data quality check within your Airflow DAG. How can you access detailed information about the error to aid in troubleshooting?
  • A. The Airflow web UI  task details page might display error messages related to the failed check.
  • B. All of the above
  • C. The Airflow logs (scheduler, worker) might contain relevant error messages from the failing task.
  • D. You can directly inspect the custom Python script used for the check to identify potential issues.
Answer: B
Explanation:
While option D might be desirable if both approaches are applicable, storing sensitive data in code is insecure (option D)

NEW QUESTION # 210
Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
  • A. They increase the amount of data shuffle during joins.
  • B. They reduce the reliability of Spark applications.
  • C. They reduce network I/O by making data available locally on each node.
  • D. They are automatically cleaned up after each task.
Answer: C
Explanation:
Broadcast variables are used in Spark to cache static lookup tables on each node, rather than sending this data with every task. This approach reduces network I/O by making the data available locally, which is particularly beneficial for tasks that need to access the same data repeatedly, such as when performing map-side joins or lookup operations.

NEW QUESTION # 211
In the context of Spark SQL, what does the Catalyst optimizer use to optimize queries?
  • A. Machine learning algorithms to predict the fastest query execution plan
  • B. A rule-based optimization model that applies predefined rules to simplify queries
  • C. A cost-based optimization model that considers the size of intermediate data
Answer: B,C
Explanation:
The Catalyst optimizer in Spark SQL uses both a cost-based optimization model and a rule-based optimization model. The cost-based model considers factors like the size of intermediate data to choose the most efficient query execution plan, while the rule-based model applies a series of predefined rules to simplify and optimize queries. This combination allows Spark SQL to efficiently execute queries by optimizing both the logical and physical plans. Machine learning algorithms (Option C. are not used by the Catalyst optimizer for this purpose.

NEW QUESTION # 212
You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?
  • A. Use rdd.persist() with the appropriate storage level based on your needs
  • B. Implement custom logic to save the data to HDFS between stages
  • C. Leverage Spark's automatic checkpointing mechanism
  • D. Store the data in a temporary table using Spark SQL
Answer: A
Explanation:
While other options might work, rdd.persist() is the recommended approach for Spark's distributed persistence. It allows you to specify the storage level (e.g., MEMORY_ONLY, MEMORY_AND_DISK) for intermediate RDDs, ensuring they are available for future stages without recomputing, improving efficiency and performance.

NEW QUESTION # 213
......
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