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[Hardware] CDP-3002 Pdf Demo Download Pass Certify| Efficient Reliable CDP-3002 Exam Camp:

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【Hardware】 CDP-3002 Pdf Demo Download Pass Certify| Efficient Reliable CDP-3002 Exam Camp:

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q27-Q32):NEW QUESTION # 27
When using Apache Airflow to schedule quality checks, which strategy helps ensure that checks are only run on the most recent data partition?
A Use the LatestOnlyOperator to skip tasks that are not the latest in a series of executions.
  • A. Implement a custom PythonOperator that queries the database for the latest partition.
  • B. Apply the depends_on_past=True parameter to the quality check task.
  • C. Utilize the execution_date in your SQL query to target the latest partition.
Answer: C
Explanation:
The LatestOnlyOperator in Apache Airflow allows you to ensure that certain tasks are only executed in the most recent DAG run. This is useful for quality checks that should only be applied to the latest data partition, avoiding unnecessary checks on historical data.

NEW QUESTION # 28
What is a potential drawback of improperly configured bucketing in Hive that could negate performance benefits?
  • A. Mandatory manual intervention for each query execution
  • B. Increased risk of data loss due to bucket corruption
  • C. Automatic conversion of bucketed tables to non-bucketed tables
  • D. Skewed data distribution leading to uneven load across nodes
Answer: D
Explanation:
A potential drawback of improperly configured bucketing in Hive is skewed data distribution, which can lead to an uneven load across nodes. If the bucketing column or the number of buckets is not chosen carefully, it may result in certain buckets being significantly larger than others. This imbalance can negate the performance benefits of bucketing by causing some nodes to do much more work than others, leading to bottlenecks and inefficient resource utilization during data processing tasks.

NEW QUESTION # 29
When configuring a Spark job to run on Kubernetes within the Cloudera Data Engineering (CDE. service, which property is crucial for specifying the Docker image to be used by the executors?
  • A. spark.kubernetes.container.image
  • B. spark.executor.image
  • C. spark.cde.container.image
  • D. spark.kubernetes.executor.container.image
Answer: D
Explanation:
The correct property for specifying the Docker image to be used by the executors when configuring a Spark job to run on Kubernetes is spark.kubernetes.executor.container.image. This property allows you to define the specific Docker image that Spark executors will use when the job is deployed on a Kubernetes cluster, ensuring that your Spark jobs have the necessary environment and dependencies required for execution.

NEW QUESTION # 30
Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.
  • A. Both A and C.
  • B. Spark can optimize data access patterns for both wide and narrow tables equally.
  • C. Wide tables require more storage and may lead to performance overhead due to data serialization.
  • D. Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
Answer: A
Explanation:
Wide tables can lead to performance inefficiencies due to larger data serialization costs in shuffle operations. Narrow tables, however, may require more complex logic for joining data from multiple columns. Choosing the optimal format depends on access patterns and trade-offs in storage and processing efficiency.

NEW QUESTION # 31
Which of the following best describes the benefit of partition pruning in Spark SQL?
  • A. It decreases the amount of data shuffled during join operations.
  • B. It increases the number of partitions to maximize parallelism.
  • C. It dynamically adjusts partition sizes based on the query execution plan.
  • D. It selectively processes partitions that meet certain criteria, reducing 1/0.
Answer: D
Explanation:
Partition pruning is a performance optimization in Spark SQL that allows Spark to read only a subset of partitions from a DataFrame or table that meet certain criteria specified in the query. This significantly reduces the amount of data read from disk and processed, thereby decreasing I/O and improving query performance. Partition pruning is particularly effective for queries with filters that can be applied to partitioned columns. Options A and B describe different aspects of performance optimization but are not directly related to partition pruning. Option D suggests an adaptive behavior not specifically tied to the concept of partition pruning.

NEW QUESTION # 32
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