1
PracticeExam
Databricks Certified Associate Developer for Apache Spark 3.0 – Python
NOTE: ANSWERS KEY PROVIDED ON LAST PAGES
Overview
This is a practice exam for the Databricks Certified Associate Developer for Apache Spark 3.0 -
Python exam. The questions here are retired questions from the actual exam that are
representative of the questions one will receive while taking the actual exam. After taking this
practice exam, one should know what to expect while taking the actual Associate Developer for
Apache Spark 3.0 - Python exam.
Just like the actual exam, it contains 60 multiple-choice questions. Each of these questions has
one correct answer. The correct answer for each question is listed at the bottom in the Correct
Answers section.
There are a few more things to be aware of:
1. This practice exam is for the Python version of the actual exam, but it’s incredibly similar to
the Scala version of the actual exam, as well. There is a practice exam for the Scala version,
too.
2. There is a two-hour time limit to take the actual exam.
3. In order to pass the actual exam, testers will need to correctly answer at least 42 of the 60
questions.
4. During the actual exam, testers will be able to reference a PDF version of the Apache Spark
documentation. Please use this version of the documentation while taking this practice
exam.
5. During the actual exam, testers will not be able to test code in a Spark session. Please do
not use a Spark session when taking this practice exam.
,2
6. These questions are representative of questions that are on the actual exam, but they are
no longer on the actual exam.
Exam Questions
Question 1
Which of the following statements about the Spark driver is incorrect?
A. The Spark driver is the node in which the Spark application's main method runs to
coordinate the Spark application.
B. The Spark driver is horizontally scaled to increase overall processing throughput.
C. The Spark driver contains the SparkContext object.
D. The Spark driver is responsible for scheduling the execution of data by various worker
nodes in cluster mode.
E. The Spark driver should be as close as possible to worker nodes for optimal performance.
Question 2
Which of the following describes nodes in cluster-mode Spark?
A. Nodes are the most granular level of execution in the Spark execution hierarchy.
B. There is only one node and it hosts both the driver and executors.
C. Nodes are another term for executors, so they are processing engine instances for
performing computations.
D. There are driver nodes and worker nodes, both of which can scale horizontally.
E. Worker nodes are machines that host the executors responsible for the execution of tasks.
Question 3
Which of the following statements about slots is true?
A. There must be more slots than executors.
B. There must be more tasks than slots.
C. Slots are the most granular level of execution in the Spark execution hierarchy.
D. Slots are not used in cluster mode.
E. Slots are resources for parallelization within a Spark application.
, 3
Question 4
Which of the following is a combination of a block of data and a set of transformers that will run on
a single executor?
A. Executor
B. Node
C. Job
D. Task
E. Slot
Question 5
Which of the following is a group of tasks that can be executed in parallel to compute the same set
of operations on potentially multiple machines?
A. Job
B. Slot
C. Executor
D. Task
E. Stage
Question 6
Which of the following describes a shu e?
A. A shu e is the process by which data is compared across partitions.
B. A shu e is the process by which data is compared across executors.
C. A shu e is the process by which partitions are allocated to tasks.
D. A shu e is the process by which partitions are ordered for write.
E. A shu e is the process by which tasks are ordered for execution.
Question 7
DataFrame df is very large with a large number of partitions, more than there are executors in the
cluster. Based on this situation, which of the following is incorrect? Assume there is one core per
executor.
A. Performance will be suboptimal because not all executors will be utilized at the same time.
B. Performance will be suboptimal because not all data can be processed at the same time.
C. There will be a large number of shu e connections performed on DataFrame df when
operations inducing a shu e are called.
PracticeExam
Databricks Certified Associate Developer for Apache Spark 3.0 – Python
NOTE: ANSWERS KEY PROVIDED ON LAST PAGES
Overview
This is a practice exam for the Databricks Certified Associate Developer for Apache Spark 3.0 -
Python exam. The questions here are retired questions from the actual exam that are
representative of the questions one will receive while taking the actual exam. After taking this
practice exam, one should know what to expect while taking the actual Associate Developer for
Apache Spark 3.0 - Python exam.
Just like the actual exam, it contains 60 multiple-choice questions. Each of these questions has
one correct answer. The correct answer for each question is listed at the bottom in the Correct
Answers section.
There are a few more things to be aware of:
1. This practice exam is for the Python version of the actual exam, but it’s incredibly similar to
the Scala version of the actual exam, as well. There is a practice exam for the Scala version,
too.
2. There is a two-hour time limit to take the actual exam.
3. In order to pass the actual exam, testers will need to correctly answer at least 42 of the 60
questions.
4. During the actual exam, testers will be able to reference a PDF version of the Apache Spark
documentation. Please use this version of the documentation while taking this practice
exam.
5. During the actual exam, testers will not be able to test code in a Spark session. Please do
not use a Spark session when taking this practice exam.
,2
6. These questions are representative of questions that are on the actual exam, but they are
no longer on the actual exam.
Exam Questions
Question 1
Which of the following statements about the Spark driver is incorrect?
A. The Spark driver is the node in which the Spark application's main method runs to
coordinate the Spark application.
B. The Spark driver is horizontally scaled to increase overall processing throughput.
C. The Spark driver contains the SparkContext object.
D. The Spark driver is responsible for scheduling the execution of data by various worker
nodes in cluster mode.
E. The Spark driver should be as close as possible to worker nodes for optimal performance.
Question 2
Which of the following describes nodes in cluster-mode Spark?
A. Nodes are the most granular level of execution in the Spark execution hierarchy.
B. There is only one node and it hosts both the driver and executors.
C. Nodes are another term for executors, so they are processing engine instances for
performing computations.
D. There are driver nodes and worker nodes, both of which can scale horizontally.
E. Worker nodes are machines that host the executors responsible for the execution of tasks.
Question 3
Which of the following statements about slots is true?
A. There must be more slots than executors.
B. There must be more tasks than slots.
C. Slots are the most granular level of execution in the Spark execution hierarchy.
D. Slots are not used in cluster mode.
E. Slots are resources for parallelization within a Spark application.
, 3
Question 4
Which of the following is a combination of a block of data and a set of transformers that will run on
a single executor?
A. Executor
B. Node
C. Job
D. Task
E. Slot
Question 5
Which of the following is a group of tasks that can be executed in parallel to compute the same set
of operations on potentially multiple machines?
A. Job
B. Slot
C. Executor
D. Task
E. Stage
Question 6
Which of the following describes a shu e?
A. A shu e is the process by which data is compared across partitions.
B. A shu e is the process by which data is compared across executors.
C. A shu e is the process by which partitions are allocated to tasks.
D. A shu e is the process by which partitions are ordered for write.
E. A shu e is the process by which tasks are ordered for execution.
Question 7
DataFrame df is very large with a large number of partitions, more than there are executors in the
cluster. Based on this situation, which of the following is incorrect? Assume there is one core per
executor.
A. Performance will be suboptimal because not all executors will be utilized at the same time.
B. Performance will be suboptimal because not all data can be processed at the same time.
C. There will be a large number of shu e connections performed on DataFrame df when
operations inducing a shu e are called.
