Databricks-Spark-Assoc Using Spark SQL Practice Question
What is the primary purpose of the 'Cache' command in Spark SQL?
⚠ Common exam trap
Examinees often confuse caching with permanent storage or assume it automatically speeds up every single-use query without needing an iterative context.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
To keep the data in memory to avoid redundant re-computation.
Caching allows developers to persist frequently accessed data in memory across multiple actions. By avoiding repeated reads from storage and re-computation of transformations, caching significantly speeds up iterative workloads, such as machine learning training or complex dashboard refreshes. However, it must be used judiciously, as memory is a finite resource, and unnecessary caching can lead to OOM errors and overall performance degradation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To permanently store the data on the disk for future use.
Why it's wrong here
Caching does not write data to disk for permanent storage; it stores it in the memory (or optionally, disk) of the executors for the current session. Permanent storage is handled by 'saveAsTable' or similar write operations. Caching is a transient optimization for performance, not a data persistence strategy.
- ✓
To keep the data in memory to avoid redundant re-computation.
Why this is correct
The primary goal of caching is to keep a computed DataFrame in memory so that subsequent actions triggered on that data do not need to re-execute the entire lineage of transformations. This is crucial for performance when the same data is used multiple times within a job.
- ✗
To force the garbage collector to free up memory immediately.
Why it's wrong here
Caching actually consumes memory rather than freeing it. It does not control garbage collection behavior. In fact, aggressive caching without considering memory limits can cause the Java Virtual Machine (JVM) to trigger frequent garbage collection, which can slow down the entire cluster rather than improve performance.
- ✗
To automatically partition the data across the cluster for faster access.
Why it's wrong here
Caching does not change the partitioning scheme of the data. It merely stores the existing partitions in memory. To optimize parallelism, developers should use 'repartition' or 'coalesce' before caching, but the 'cache' command itself is strictly focused on memory persistence, not data distribution or layout restructuring.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.