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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
In Spark, when is it most beneficial to use the repartitionByRange method?

A. When minimizing network traffic during shuffle operations is the primary concern.
B. When decreasing the number of partitions to reduce task scheduling overhead.
C. When the data is highly skewed and a uniform distribution of data across partitions is required.
D. When sorting data within each partition by a specified column or set of columns is required.

Answer: D

Explanation:
The repartitionByRange method is used to repartition the data based on a range of values, which effectively sorts the data within each partition according to the specified columns. This can be beneficial for performance optimization in scenarios where sorted data can improve the efficiency of subsequent operations, such as range-based queries or when preparing data for window functions. While Option A mentions data skew, repartitionByRange is specifically designed for sorting rather than uniformly distributing skewed data. Option B and D address different concerns related to shuffle operations and task scheduling, which are not the primary purposes of repartitionByRange.

NEW QUESTION # 137
How does partition pruning contribute to query performance in the Cloudera Data Platform?

A. By selecting only relevant partitions for query execution
B. By encrypting data at rest
C. By removing unnecessary data replication
D. By increasing the number of partitions

Answer: A

Explanation:
Partition pruning enhances query performance by allowing the engine to skip over partitions of the data that are not relevant to the query, thereby reducing the amount of data scanned and processed.

NEW QUESTION # 138
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 cost-based optimization model that considers the size of intermediate data
C. A rule-based optimization model that applies predefined rules to simplify queries

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 # 139
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. Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
B. Both A and C.
C. Wide tables require more storage and may lead to performance overhead due to data serialization.
D. Spark can optimize data access patterns for both wide and narrow tables equally.

Answer: B

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 # 140
You're integrating data quality checks into a complex ETL pipeline with numerous tasks and dependencies. How can you ensure the checks are executed in the correct order and don't interfere with other pipeline tasks?

A. Implement a custom script to manage the execution of the data quality checks independently.
B. Schedule the data quality checks as a separate DAG and trigger it after the ETL pipeline completes.
C. Run all tasks (ETL and checks) concurrently, assuming they are independent.
D. Utilize Airflow upstream/downstream dependencies to define the execution order between check tasks and other pipeline tasks.

Answer: D

Explanation:
While option A might be a workaround, relying on separate DAGs can lead to management complexity. Option B highlights the importance of using Airflow's dependency features:

NEW QUESTION # 141
......

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CDP-3002 Study Tool | PDF CDP-3002 VCE

CDP Data Engineer - Certification Exam valid practice questions & CDP-3002 exam pdf torrent & CDP Data Engineer - Certification Exam latest study dumps

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q136-Q141):

Earn The Badge Of Cloudera CDP-3002 Certification Exam On The First Attempt

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