What is the primary purpose of calling cache() on a DataFrame in Spark?

To store the DataFrame in memory so that subsequent actions on it are faster.

To shuffle the data across partitions for better parallelism.

To force the DataFrame to be written to disk for fault tolerance.

To release memory used by the DataFrame.

A Spark application processes a large stream of events and performs many short-lived map operations. The driver logs show frequent full garbage collections that cause executor tasks to pause for several seconds. The team suspects the default JVM heap settings are causing excessive GC pressure. Which tuning approach is most likely to reduce the pauses?

Reduce the number of partitions to decrease object churn

Increase the executor memory and rely on the default ParallelGC collector

Disable garbage collection entirely by setting -XX:+UseSerialGC

Use the G1GC garbage collector and tune -XX:MaxGCPauseMillis to a lower target

DatabricksCertified Associate Developer for Apache Spark

Domain 1Objective 3

Describe the Architecture of Apache Spark™, Including DataFrame and Dataset Concepts, SparkSession Lifecycle, Caching, Storage Levels, and Garbage Collection. 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 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

6concepts

20%of the exam

Questions 21–24

21foundation · easy
In the Apache Spark architecture, which component is responsible for scheduling tasks and coordinating the execution of a Spark application?
Select an answer first
22application · medium
A team is caching a DataFrame that is read multiple times. The data is large, and the cluster has limited memory. They want to minimize the memory footprint while still avoiding recomputation. Which storage level is most appropriate?
Select an answer first
23foundation · easy
What is the primary purpose of calling cache() on a DataFrame in Spark?
Select an answer first
24expert · hard
A Spark application processes a large stream of events and performs many short-lived map operations. The driver logs show frequent full garbage collections that cause executor tasks to pause for several seconds. The team suspects the default JVM heap settings are causing excessive GC pressure. Which tuning approach is most likely to reduce the pauses?
Select an answer first

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Describe the Architecture of Apache Spark™, Including DataFrame and Dataset Concepts, SparkSession Lifecycle, Caching, Storage Levels, and Garbage Collection. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 5)

Questions 21–24

In the Apache Spark architecture, which component is responsible for scheduling tasks and coordinating the execution of a Spark application?

A team is caching a DataFrame that is read multiple times. The data is large, and the cluster has limited memory. They want to minimize the memory footprint while still avoiding recomputation. Which storage level is most appropriate?

What is the primary purpose of calling cache() on a DataFrame in Spark?

Keep studying — Apache Spark Architecture and Components