Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer writes a Spark Connect application that calls `df.cache()` on a large DataFrame, then performs several transformations and an action. The developer expects the cached data to persist on the client for reuse across sessions. Which statement describes what actually happens?
⚠ Common exam trap
The trap here is assuming the thin client holds cached data locally, when caching actually occurs on the server within the session's lifetime.
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 request is sent to the server, where the data is cached in the cluster's memory or disk, and it persists only for the lifetime of that server-side session.
Caching in Spark Connect is a server-side operation. When a client calls `cache`, the request is transmitted to the server, which stores the data according to the specified storage level within that session. The cache is not stored on the thin client and does not survive beyond the server-side session's lifetime, so reuse across separate client sessions is not possible.
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 call is silently ignored because Spark Connect does not support caching DataFrames.
Why it's wrong here
Spark Connect supports caching through the DataFrame API. The operation is translated into a server-side plan, and the server manages the cached data. Ignoring the call would be incorrect behavior, and caching is a documented capability of the API.
- ✗
Caching is only supported for temporary views and fails when called directly on a DataFrame in Spark Connect.
Why it's wrong here
Caching can be applied to DataFrames directly, and the server handles it as a storage-level request. There is no restriction limiting caching to temporary views only. The DataFrame API includes `cache` and `persist` methods that operate through the Connect protocol.
- ✗
The cache is stored on the client machine, so subsequent sessions on the same laptop can reuse it without recomputation.
Why it's wrong here
The client is a thin process and does not store DataFrame data for caching. Caching is a server-side operation managed by the Spark Connect server. Client-side persistence of cached data is not part of the architecture, so this expectation is incorrect for the scenario.
- ✓
The cache request is sent to the server, where the data is cached in the cluster's memory or disk, and it persists only for the lifetime of that server-side session.
Why this is correct
In Spark Connect, `cache()` sends a plan to the server that marks the DataFrame for caching. The server stores the data according to the storage level within the cluster. The cache is tied to the server-side session and is not available to other client sessions or after the session ends.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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