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Databricks Certified Associate Developer for Apache Spark 3.0: Certified Associate Developer for Apache Spark
Free Practice Exam Questions (page: 4)
Updated On: 2-Jan-2026

Page 4 of 46
PDF with 342 Questions

Which of the following describes the difference between client and cluster execution modes?

  1. In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.
  2. In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
  3. In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
  4. In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
  5. In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

Answer(s): A

Explanation:

In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
This is wrong, since execution modes do not specify whether workloads are run in the cloud or on- premise.
In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
Wrong, since in both cases executors run on worker nodes.
In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
Wrong – in cluster mode, the driver runs on a worker node. In client mode, the driver runs on the client machine.
In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
No. In both modes, the cluster manager is typically on a separate node – not on the same host as the driver. It only runs on the same host as the driver in local execution mode.
More info: Learning Spark, 2nd Edition, Chapter 1, and Spark: The Definitive Guide, Chapter 15. ()



Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

  1. Slot is another name for executor.
  2. There must be less executors than tasks.
  3. An executor runs on a single core.
  4. There must be more slots than tasks.
  5. Tasks run in parallel via slots.

Answer(s): E

Explanation:

Tasks run in parallel via slots.
Correct. Given the assumption, an executor then has one or more "slots", defined by the equation spark.executor.cores / spark.task.cpus. With the executor's resources divided into slots, each task takes up a slot and multiple tasks can be executed in parallel. Slot is another name for executor.
No, a slot is part of an executor. An executor runs on a single core.
No, an executor can occupy multiple cores. This is set by the spark.executor.cores option. There must be more slots than tasks.
No. Slots just process tasks. One could imagine a scenario where there was just a single slot for multiple tasks, processing one task at a time. Granted – this is the opposite of what Spark should be used for, which is distributed data processing over multiple cores and machines, performing many tasks in parallel.
There must be less executors than tasks. No, there is no such requirement.
More info: Spark Architecture | Distributed Systems Architecture (https://bit.ly/3x4MZZt)



Which of the following statements about RDDs is incorrect?

  1. An RDD consists of a single partition.
  2. The high-level DataFrame API is built on top of the low-level RDD API.
  3. RDDs are immutable.
  4. RDD stands for Resilient Distributed Dataset.
  5. RDDs are great for precisely instructing Spark on how to do a query.

Answer(s): A

Explanation:

An RDD consists of a single partition.
Quite the opposite: Spark partitions RDDs and distributes the partitions across multiple nodes.



Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?

  1. 1, 10
  2. 1, 8
  3. 10
  4. 7, 9, 10
  5. 1, 4, 6, 9

Answer(s): B

Explanation:

1: Correct – This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a DataFrame accessible via SQL, you first need to create a DataFrame view. That

view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column. 6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally wrong with using this type here.
8: Correct – TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types - Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)



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