Databricks-Spark-Assoc Pandas API on Spark Practice Test 3 — 15 Questions

Question 1

You have a Pandas API on Spark DataFrame `psdf` that was created from a Spark DataFrame with 200 partitions. You call `psdf.head(10)` in a Databricks notebook. What is the most likely performance characteristic of this operation?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

It collects only the necessary rows from the first partition(s) and returns quickly without scanning the entire dataset.. The `head` operation in Pandas API on Spark is designed to be efficient by leveraging Spark's `limit` operator, which reads only the necessary rows from the first partitions. It does not scan the entire dataset or perform a full shuffle. This makes it suitable for quickly inspecting large DataFrames without incurring heavy computation costs.

Answer

It shuffles all data to a single partition before returning the first 10 rows, causing a full data movement.

Answer

It fails with an error because `head` is not supported on DataFrames with more than 100 partitions.

Answer

It triggers a full scan of all 200 partitions to ensure the first 10 rows are correctly ordered.

Question 2

A developer is using Pandas API on Spark and wants to convert a Pandas-on-Spark DataFrame `psdf` back to a standard pandas DataFrame for local analysis. Which method should they use?

Accepted Answer

`psdf.toPandas()`. The `toPandas()` method collects the distributed data into a single pandas DataFrame on the driver. It is the standard way to convert from Pandas API on Spark to local pandas. However, it should be used cautiously with large datasets to avoid memory issues.

Answer

`psdf.collect()`

Answer

`psdf.to_pandas()`

Answer

`psdf.toDF()`

Question 3

A data engineer is using Pandas API on Spark and needs to perform a join between two Pandas-on-Spark DataFrames `psdf1` and `psdf2` on a common column `id`. They write the following code:

```python
result = psdf1.merge(psdf2, on='id', how='inner')
```

Which statement best describes the execution and potential issue with this operation?

Accepted Answer

The merge is executed as a distributed sort-merge join, which may require a shuffle of both DataFrames across the network.. The merge operation in Pandas API on Spark translates to a Spark join, which typically involves shuffling data to co-locate matching keys. This distributed execution allows handling large datasets but can introduce network overhead. The actual join strategy depends on data size and Spark configuration, but a shuffle is common.

Answer

The merge is performed locally on the driver after collecting both DataFrames, which can cause out-of-memory errors.

Answer

The merge automatically broadcasts the smaller DataFrame to all nodes, avoiding a shuffle.

Answer

The merge requires that both DataFrames have the same number of partitions, otherwise it fails with an error.

Question 4

A data engineer is using Pandas API on Spark to process a large dataset. They call `psdf.to_pandas()` on a DataFrame that is 50 GB in size. What is the most likely outcome?

Accepted Answer

The operation fails with an OutOfMemoryError because the entire dataset is collected into the driver's memory.. `to_pandas()` is a collect operation that brings all data to the driver as a pandas DataFrame. For large datasets, this will exceed driver memory and cause an OutOfMemoryError. It is intended for small results, not for converting massive distributed DataFrames. Alternative approaches like sampling or aggregating before collection should be used.

Answer

The operation succeeds only if the DataFrame is cached in memory beforehand.

Answer

The operation completes successfully but takes a long time due to the large data volume.

Answer

The operation automatically partitions the data and returns a list of smaller pandas DataFrames.

Question 5

A developer is working with a Pandas API on Spark DataFrame `psdf` and wants to perform operations that are efficient in a distributed environment. Which two operations are considered efficient and do not require collecting data to the driver? (Choose two.)

Accepted Answer

`psdf.merge(other_psdf, on='id')`. Distributed operations like `groupby().agg()` and `merge()` execute in parallel across the cluster and return distributed DataFrames, avoiding driver collection. In contrast, `apply` with a lambda, `head`, and `to_pandas` either collect data to the driver or force row-by-row processing, making them less efficient for large-scale data.

Answer

`psdf.to_pandas()`

Answer

`psdf.head(20)`

Answer

`psdf['value'].apply(lambda x: x * 2)`

Question 6

A data analyst wants to use Pandas API on Spark in a Databricks notebook but is unsure how to import it. Which import statement correctly enables the Pandas API on Spark?

Accepted Answer

`import pyspark.pandas as ps`. Pandas API on Spark is accessed via the `pyspark.pandas` module. Importing it as `ps` is the standard convention. This module provides a pandas-like interface that translates operations to Spark, enabling distributed processing. Other imports either refer to standard pandas or non-existent modules.

Answer

`import databricks.pandas as dp`

Answer

`import pandas as pd`

Answer

`from pyspark.sql import PandasAPI`

Question 7

A data analyst is using the Pandas API on Spark to compute summary statistics. They call `psdf.describe()` on a large DataFrame and notice the job takes much longer than expected. They want to understand why this operation is more expensive than a similar operation on a small local pandas DataFrame. What is the primary reason?

Accepted Answer

`describe()` triggers a full scan and computes multiple aggregations that may require shuffling data across partitions.. `describe()` on a Pandas API on Spark DataFrame runs a distributed Spark job that scans all rows and computes multiple aggregates. Percentiles require approximate quantile algorithms that shuffle data across partitions. This is fundamentally more expensive than local pandas, which operates on in-memory data on a single machine without network shuffles.

Answer

`describe()` is a lazy operation that only builds a plan, so the delay is from plan construction rather than execution.

Answer

`describe()` caches the DataFrame in memory by default, and the caching step is what takes the extra time.

Answer

`describe()` converts the entire DataFrame to a local pandas DataFrame on the driver before computing statistics.

Question 8

A data engineer is processing a 500 GB Parquet dataset on Databricks using the Pandas API on Spark. They need to extract a single scalar value, the maximum timestamp, to pass to a downstream orchestration tool. They use psdf['timestamp'].max(). Which statement correctly describes how this operation executes?

Accepted Answer

It is an immediate, blocking operation that triggers a Spark job and returns a Python scalar.. Scalar-returning reductions in the pandas API on Spark, such as Series.max(), are eager: they immediately trigger a Spark job and return a local Python scalar. They do not return a lazy distributed object, because a single value has no distributed representation. This behavior aligns with pandas and is important when mixing pandas-on-Spark with orchestration code that expects a concrete value.

Answer

It is executed locally by converting the entire column to a pandas Series on the driver before computing the maximum.

Answer

It returns a new single-row pandas-on-Spark DataFrame that must be collected to obtain the value.

Answer

It is executed lazily and returns a scalar only when the DataFrame is collected or persisted to storage.

Question 9

A data engineer has a Pandas-on-Spark DataFrame `psdf` with a column `event_ts` stored as string timestamps. They run `psdf['event_ts'].astype('datetime64[ns]')` and then call `.dt.hour` on the resulting Series. In a Databricks notebook, what is the result of this operation?

Accepted Answer

The conversion and `.dt.hour` execute as Spark expressions, returning a new Pandas-on-Spark Series without collecting data to the driver.. Pandas API on Spark translates supported pandas operations into Spark logical plans. Casting a string column to datetime64 and accessing `.dt.hour` are both supported and map to Spark's timestamp cast and hour extraction. The result stays distributed as a Pandas-on-Spark Series, and no driver collection is triggered. This preserves scalability and aligns with the library's goal of providing pandas-like syntax over Spark execution.

Answer

The operation succeeds only if `spark.sql.execution.arrow.pyspark.enabled` is set to true; otherwise it falls back to a Python UDF that may fail on null timestamps.

Answer

The operation triggers an immediate collect of all rows to the driver, converts them to pandas, computes the hour locally, and returns a pandas Series.

Answer

The `.dt` accessor is unsupported in Pandas API on Spark, so the call raises an AttributeError before any Spark job is launched.

Question 10

A developer is working with a Pandas API on Spark DataFrame `psdf` that has a default index. They call `psdf.sort_values('amount')` and then attempt to use `.loc` with an integer label to retrieve a specific row. They find that the integer label does not correspond to the row position they expect. What is the most likely explanation?

Accepted Answer

The default index in the Pandas API on Spark is a sequence index that is not guaranteed to match row order after operations like `sort_values`.. The default index in the Pandas API on Spark is a synthetic sequence that is not renumbered after operations like `sort_values`. As a result, integer labels do not reliably map to row positions. To access rows by position, use `.iloc`. To make labels meaningful, explicitly set or reset the index before relying on label-based access.

Answer

The `.loc` indexer in the Pandas API on Spark always uses positional indexing, so integer labels are treated as row positions.

Answer

The default index is a distributed index that is only valid on the driver, so `.loc` cannot use it on executors.

Answer

`sort_values` in the Pandas API on Spark resets the index by default, so integer labels always match the new sorted positions.

Question 11

A developer is using the pandas API on Spark in a Databricks notebook. They have a pandas-on-Spark DataFrame psdf with a default index. They call psdf.sort_values('amount') and then psdf.head(10). They observe that the resulting index values are not sequential from 0 to 9, but instead appear as arbitrary integers. What is the most likely explanation for this behavior?

Accepted Answer

The default index type is 'distributed', which assigns arbitrary but stable integers to rows and does not guarantee sequential values after operations like sort_values.. The default index type in pandas API on Spark is 'distributed', which assigns stable but non-sequential integers to rows. When you sort a DataFrame, the existing index values travel with the rows, so the resulting index is not 0..n-1. To get sequential indices after sorting, you must explicitly call reset_index() or set the index type to 'distributed-sequence' before sorting.

Answer

The default index type is 'distributed-sequence', which preserves the original row order and does not reassign new sequential indices after sorting.

Answer

The default index type is 'sequence', which creates a single-partition sequence index that is lost during shuffles such as sorting.

Answer

The default index type is 'distributed-sequence', but sort_values triggers a shuffle that resets the index to arbitrary values because sequence indices cannot survive shuffles.

Question 12

A developer is using Pandas API on Spark in a Databricks notebook and needs to combine two Pandas-on-Spark DataFrames that originate from different Spark DataFrame ancestors. They encounter a `compute.ops_on_diff_frames` error. Which two actions will resolve this error? (Choose two.)

Accepted Answer

Convert one of the DataFrames to a pandas DataFrame using `.to_pandas()` and then create a new Pandas-on-Spark DataFrame from it.. The `compute.ops_on_diff_frames` error occurs when Pandas-on-Spark objects derive from different Spark DataFrame ancestors. Two valid resolutions are to break the lineage conflict: either collect one frame to pandas and recreate a Pandas-on-Spark object, or drop to the underlying Spark DataFrames and combine them with PySpark operations. Both approaches produce a single consistent lineage, allowing the operation to complete.

Answer

Call `.cache()` on both DataFrames before the operation to align their internal anchors.

Answer

Enable the Spark configuration `spark.databricks.pandas.enableArrow` to allow cross-frame operations.

Answer

Set `spark.sql.execution.arrow.pyspark.enabled` to true and retry the operation.

Question 13

A developer is using the Pandas API on Spark and wants to write efficient code. They are reviewing operations that can cause a full shuffle of data across the cluster. Which TWO operations should they be cautious about because they typically require a shuffle? (Choose two.)

Accepted Answer

`psdf.groupby('region').sum()`. Operations that require global data reorganization, such as sorting and grouping, trigger shuffles. `sort_values` needs a global order, and `groupby` with aggregation needs rows with the same key co-located. By contrast, element-wise arithmetic, column selection, and `head` are narrow transformations that operate within partitions without moving data across the network.

Answer

`psdf[['id', 'amount']]`

Answer

`psdf['amount'] + 10`

Answer

`psdf.head(5)`

Question 14

A data scientist is working with a pandas-on-Spark DataFrame psdf that has a column 'category' with many unique values. They want to apply a custom Python function to each group to compute a complex statistic. They consider using psdf.groupby('category').apply(my_func). Which statement accurately describes the execution and potential performance implications of this operation?

Accepted Answer

It triggers a full shuffle to group data, then applies my_func to each group as a pandas DataFrame on the executor, and the function's return type determines the result schema.. groupby().apply() in pandas API on Spark shuffles data to group rows, then applies the function to each group as a pandas DataFrame on executors. This enables complex group-wise logic but can be slow and memory-intensive because it involves a shuffle and materializes each group in memory. The return type of the function determines the output schema, and the operation is not optimized by Catalyst.

Answer

It applies my_func in a distributed manner across partitions without shuffling, and the function must return a scalar or a pandas Series.

Answer

It uses the Spark Catalyst optimizer to translate my_func into native Spark expressions, so no Python UDF is involved and performance is optimal.

Answer

It applies my_func to each partition independently without grouping, and the results are concatenated, which is efficient for large datasets.

Question 15

A developer is using Pandas API on Spark and needs to perform operations that involve multiple DataFrames. They encounter a 'compute.ops_on_diff_frames' error. Which two actions can resolve this error? (Choose two.)

Accepted Answer

Set the configuration 'compute.ops_on_diff_frames' to True using spark.conf.set.. The 'compute.ops_on_diff_frames' error occurs when operations involve columns from different Pandas API on Spark DataFrames. The two valid solutions are to enable the configuration 'compute.ops_on_diff_frames' to True, which allows such operations, or to convert the DataFrames to PySpark DataFrames and use Spark SQL functions, which bypasses the Pandas API on Spark restriction. The other options are either not general solutions or not scalable.

Answer

Ensure both DataFrames are derived from the same base DataFrame or have the same index.

Answer

Use the 'psdf1.merge(psdf2)' method instead of direct comparison.

Answer

Call 'psdf1.to_pandas()' and 'psdf2.to_pandas()' to perform the operation in pandas.