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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0

Practice Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Questions

1. Page: 1/36
  Total 180 Questions
Question No 1

Which of the following options describes the responsibility of the executors in Spark?

Choose the Choices:

The executors accept jobs from the driver, analyze those jobs, and return results to the driver.

The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.

The executors accept tasks from the driver, execute those tasks, and return results to the driver.

The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver.

The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

C
Question No 2

Which of the following describes the role of tasks in the Spark execution hierarchy?

Choose the Choices:

Tasks are the smallest element in the execution hierarchy.

Within one task, the slots are the unit of work done for each partition of the data.

Tasks are the second - smallest element in the execution hierarchy.

Stages with narrow dependencies can be grouped into one task.

Tasks with wide dependencies can be grouped into one stage.

A
Question No 3

Which of the following describes the role of the cluster manager?

Choose the Choices:

The cluster manager schedules tasks on the cluster in client mode.

The cluster manager schedules tasks on the cluster in local mode.

The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.

The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.

The cluster manager allocates resources to the DataFrame manager.

C
Question No 4

Which of the following is the idea behind dynamic partition pruning in Spark?

Choose the Choices:

Dynamic partition pruning is intended to skip over the data you do not need in the results of a query.

Dynamic partition pruning concatenates columns of similar data types to optimize join performance.

Dynamic partition pruning performs wide transformations on disk instead of in memory.

Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.

Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.

A
Question No 5

Which of the following is one of the big performance advantages that Spark has over Hadoop?

Choose the Choices:

Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.

Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.

Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations.

Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.

Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user - friendly API.

C
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