Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is writing a Spark Connect application that needs to read a CSV file from cloud storage and then perform a groupBy aggregation. The developer wants to minimize data transfer between the client and the server. Which of the following approaches best achieves this?
⚠ Common exam trap
The trap here is thinking that caching or converting to pandas will reduce network traffic, when actually performing the aggregation remotely is what minimizes data transfer.
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
✓
Use spark.read.csv() to load the file, then call .groupBy().agg() on the DataFrame before any action that returns data to the client.
In Spark Connect, the client sends operations to the server for execution. To minimize data transfer, transformations like groupBy and aggregation should be performed on the server-side DataFrame. Only the final aggregated results are returned to the client. Collecting raw data or using toPandas() transfers the entire dataset, which is inefficient. Caching on the server can help with repeated queries but does not reduce the initial data transfer for aggregation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use spark.read.csv() to load the file, then call .toPandas() to convert the DataFrame to a pandas DataFrame, and perform the groupBy using pandas.
Why it's wrong here
toPandas() collects the entire dataset to the client, similar to collect(), and then performs the aggregation locally. This transfers all data over the network, which is exactly what the developer wants to avoid. It also requires sufficient client memory to hold the entire dataset, which may not be feasible.
- ✗
Use spark.read.csv() to load the file, then call .cache() to cache the DataFrame on the client, and perform the groupBy on the cached data.
Why it's wrong here
cache() in Spark Connect caches the DataFrame on the server, not the client. However, caching the raw data before aggregation does not reduce the amount of data transferred when an action is called; it only speeds up repeated access. The groupBy still needs to be executed, and if done on the client, it would require transferring data.
- ✗
Use spark.read.csv() to load the file, then call .collect() to bring the data to the client, and perform the groupBy using pandas on the client.
Why it's wrong here
Collecting the entire dataset to the client and then performing groupBy with pandas defeats the purpose of Spark's distributed processing. It transfers all raw data over the network, which is inefficient and can cause memory issues on the client. This approach increases data transfer and is not recommended for large datasets.
- ✓
Use spark.read.csv() to load the file, then call .groupBy().agg() on the DataFrame before any action that returns data to the client.
Why this is correct
Performing the groupBy and aggregation on the remote DataFrame allows the server to process the data in a distributed manner. Only the aggregated results are transferred to the client when an action like show() or collect() is called. This minimizes data transfer because the heavy lifting is done server-side, and the client receives a small summary.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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