Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is using Spark Connect to run a PySpark application against a remote Databricks cluster. The application calls df.cache() on a DataFrame that is used multiple times. Which statement accurately describes how caching behaves in this scenario?
⚠ Common exam trap
The trap here is assuming that because the client is remote, caching might happen locally or be unsupported, when in fact it is executed server-side as usual.
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
✓
The cache is managed on the server side, and the cached data is stored in the cluster's memory or disk according to the configured storage level.
In Spark Connect, cache() is a server-side operation. The logical plan includes the cache directive, and the remote Spark server executes it, storing the data in cluster memory or disk according to the storage level. The client does not hold cached data, so all subsequent operations still communicate with the server. This preserves the semantics of caching while maintaining the thin-client architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cache is stored in the client process memory, allowing subsequent operations to avoid round trips to the server.
Why it's wrong here
Spark Connect clients do not store cached data in local process memory. The client only holds a logical plan and session state. Caching occurs on the server, where the data resides. Subsequent operations still require communication with the server, though the server may avoid recomputation if the cache is used. The client does not cache results locally.
- ✗
The cache is automatically persisted to cloud object storage and shared across all Spark Connect clients connected to the same cluster.
Why it's wrong here
cache() does not automatically persist to cloud object storage, nor is it shared across clients. Caching is session-specific and stored in the cluster's memory or local disk. It is not a global shared cache. Other Spark Connect clients with separate sessions would not see the cached data unless they access the same session, which is not how caching works.
- ✗
Calling cache() has no effect because Spark Connect does not support caching operations on remote DataFrames.
Why it's wrong here
Spark Connect supports cache() on DataFrames. The operation is sent to the server and executed there. It is not ignored. The server will attempt to cache the data according to the storage level, just as with a classic SparkSession. The limitation is that the cache is remote, not that caching is unsupported.
- ✓
The cache is managed on the server side, and the cached data is stored in the cluster's memory or disk according to the configured storage level.
Why this is correct
In Spark Connect, cache() is a logical plan operation sent to the server. The server executes the caching, storing the data in the cluster's memory or disk based on the storage level. The client does not hold cached data. This means cache behavior and memory management are controlled by the remote Spark server, just as in a traditional Spark application.
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JA
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.