How do wide dependencies affect stage boundaries?

They allow tasks to be pipelined without shuffling

They reduce the number of stages in a job

They have no effect on stage boundaries

They create a shuffle boundary, causing a new stage to start

A Spark application is running slowly. The Spark UI shows that a particular stage has 1000 tasks, but the cluster only has 10 cores available. What is the most likely impact on execution?

The stage will fail because there are more tasks than cores.

Tasks will be scheduled in waves, with only 10 running at a time, increasing the total runtime.

All 1000 tasks will run concurrently because Spark dynamically scales the number of cores.

The tasks will be combined into fewer tasks to match the number of cores.

A Spark application is running on a cluster with dynamic allocation enabled. The application starts with 2 executors, but the Spark UI shows that the number of executors increases to 10 during a shuffle-heavy stage. What is the most likely reason for this?

The task scheduler detected a data skew and added executors to handle it.

The DAG scheduler requested more executors to speed up the shuffle.

The executors were automatically added because the shuffle spilled to disk.

The shuffle operation increased the demand for resources, so the cluster manager allocated more executors.

What determines the boundary between two stages in a Spark job?

A narrow dependency, such as a map

A wide dependency, such as a shuffle

The number of partitions in the RDD

DatabricksCertified Associate Developer for Apache Spark

Domain 1Objective 4

Explain the Apache Spark™ Architecture Execution Hierarchy.. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 5)

Part of the Apache Spark Architecture and Components domain, which accounts for 20% of the ASSOCIATE-DEVELOPER-APACHE-SPARK exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–12 in this domain), expect 1–2 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

7concepts

20%of the exam

Questions 21–25

21foundation · easy
How do wide dependencies affect stage boundaries?
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22application · medium
A Spark application is running slowly. The Spark UI shows that a particular stage has 1000 tasks, but the cluster only has 10 cores available. What is the most likely impact on execution?
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23expert · hard
A Spark application is running on a cluster with dynamic allocation enabled. The application starts with 2 executors, but the Spark UI shows that the number of executors increases to 10 during a shuffle-heavy stage. What is the most likely reason for this?
Select an answer first
24foundation · easy
What determines the boundary between two stages in a Spark job?
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25application · medium
A Spark application is reading a directory with 500 small files. The default parallelism is 100. After reading, the DataFrame is coalesced to 50 partitions. How many tasks will be in the first stage?
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