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Cloudera Certification
CDP-3002 Exam
Cloudera.CDP-3002.v2025-11-21.q109 Dumps

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Question 51

How does setting the "priority_weight" parameter in Airflow tasks influence the scheduler's behavior?

A.It determines the order in which tasks are executed within the same DAG.
B.It allocates more CPIJ and memory resources to tasks with higher priority weights.
C.It influences the order in which tasks are selected for execution across multiple DAGs competing for resources.
D.It sets a fixed time slot during which the task must be executed, overriding the scheduler's automatic timing.

Correct Answer: C

The "priority_weight" parameter in Airflow tasks is used to influence the scheduler's decision-making when multiple tasks across different DAGs are competing for execution resources. Tasks with higher priority weights are more likely to be selected for execution first. This parameter does not affect the execution order of tasks within the same DAG or allocate more computational resources to tasks; it primarily affects the scheduler's prioritization of tasks across DAGs.

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Question 52

How does Hive handle bucketing when the data inserted into a bucketed table does not evenly distribute across the buckets?

A.Hive automatically rebalances the data across buckets using a round-robin distribution.
B.Hive rejects the data insertion and raises an error.
C.Hive distributes the data based on the hash value of the bucketing column, potentially leading to skewed buckets.
D.Hive dynamically adjusts the number of buckets to evenly distribute the data.

Correct Answer: C

Hive distributes the data into buckets based on the hash value of the bucketing column(s), and it does so regardless of how evenly the data distributes across those buckets. If the data inserted into a bucketed table does not evenly distribute across the buckets, Hive will still proceed with the insertion based on the hash values, which can lead to skewed buckets if the data distribution is not uniform. Hive does not automatically rebalance data, adjust the number of buckets, or reject the insertion due to uneven distribution.

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Question 53

How can you prevent backfilling for a specific DAG in Apache Airflow?
A Set catchup=False in the DAG's arguments.

A.Set max active runs=1.
B.Use schedule interval=None.
C.Define is backfill=False in the DAG definition.

Correct Answer: A

Setting catchup=False in the DAG's default arguments prevents Airflow from backfilling any execution dates that have not been run for the period between the DAG's start date and the current date. This is useful for scenarios where backfilling is not necessary or desired.

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Question 54

In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A.It can lead to reduced fault tolerance due to reliance on in-memory storage.
B.It may increase execution time due to overheads from frequent disk 1/0 operations.
C.It can decrease network traffic by reducing the need for data shuffling.
D.It enhances data security by storing intermediate results in encrypted form.

Correct Answer: B

Indiscriminate caching, especially with the MEMORY_AND DISK storage level, can lead to increased execution time due to the overheads associated with frequent disk I/O operations. When the memory capacity is exceeded, data is spilled to disk, which can significantly slow down data access compared to in-memory operations. While this approach ensures that the data is not lost if it exceeds memory capacity, it introduces additional latency due to disk access times.

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Question 55

What is the purpose of the Airflow XCom feature?

A.To schedule DAG runs at specific intervals
B.To enable communication between tasks in a DAG
C.To execute tasks on remote machines
D.To monitor and log the execution of DAGs

Correct Answer: B

Airflow's XCom (short for "cross-communication") feature allows tasks to share data (small amounts) such as messages or values between them, enabling inter-task communication within a DAG. This can be useful for passing results from one task to another, thereby creating a more dynamic and flexible workflow.

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