Databricks-Spark-Assoc Pandas API on Spark Practice Question
A developer is using Pandas API on Spark and encounters a `compute.ops_on_diff_frames` error when combining two Pandas-on-Spark DataFrames. Which two actions can resolve this error? (Choose two.)
⚠ Common exam trap
The trap here is thinking that repartitioning or converting to pandas resolves the anchor mismatch, when the real solutions are either enabling the specific option or unifying the DataFrames' lineage.
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
✓
Ensure both DataFrames originate from the same Spark DataFrame or are derived from a common ancestor without independent transformations.
The error arises when operations combine DataFrames with different internal anchors. Enabling `compute.ops_on_diff_frames` explicitly allows such operations, while aligning anchors by deriving from a common source avoids the error altogether. Both approaches keep computation distributed and within the Pandas API on Spark.
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 `psdf.spark.frame()` to extract the underlying Spark DataFrame and perform a join with Spark SQL.
Why it's wrong here
While extracting the Spark DataFrame and using Spark SQL can bypass the pandas API restriction, it abandons the pandas API and requires rewriting the logic. It is not a direct resolution within the Pandas API on Spark and does not address the anchor mismatch for subsequent pandas-like operations.
- ✗
Convert both DataFrames to pandas DataFrames using `to_pandas()` and then perform the operation locally.
Why it's wrong here
Converting to pandas collects all data to the driver, which is infeasible for large datasets and defeats the purpose of distributed processing. While it technically avoids the error by leaving the Pandas API on Spark context, it is not a recommended resolution and can cause out-of-memory errors.
- ✓
Ensure both DataFrames originate from the same Spark DataFrame or are derived from a common ancestor without independent transformations.
Why this is correct
Pandas API on Spark tracks lineage via an internal anchor. If both DataFrames share the same anchor, operations are allowed without the configuration flag. Deriving them from a common source or using `attach` to align anchors avoids the error and keeps execution efficient by avoiding unnecessary shuffles.
- ✗
Repartition both DataFrames to the same number of partitions before the operation.
Why it's wrong here
Repartitioning alone does not align the internal anchors used by Pandas API on Spark. The error is about lineage, not partition count. Even with identical partition counts, operations across different anchors will still raise the error unless the configuration is enabled or anchors are unified.
- ✓
Set `pyspark.pandas.options.compute.ops_on_diff_frames` to True to allow operations across different DataFrames.
Why this is correct
Enabling `compute.ops_on_diff_frames` permits operations between DataFrames that do not share the same internal anchor. This is the direct configuration change intended for such scenarios, though it may introduce additional shuffles and should be used with awareness of performance implications.
Visual reference
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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