Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps

Adaptive Query Execution (AQE) is a Spark 3.x feature that dynamically optimizes query plans at runtime. One of its core features is:

Dynamically switching join strategies (e.g., from sort-merge to broadcast) based on runtime statistics.

Other AQE capabilities include:

Coalescing shuffle partitions

Skew join handling

Option A is correct.

Option B refers to statistics collection, which is not AQE's primary function.

Option C is too broad and not AQE-specific.

Option D refers to Delta Lake optimizations, unrelated to AQE.

Final Answer: A

A broadcast join is a type of join where the smaller DataFrame is replicated (broadcast) to all worker nodes in the cluster. This avoids shuffling the large DataFrame across the network.

Benefits:

Eliminates shuffle for the smaller dataset.

Greatly improves performance when one side of the join is small enough to fit in memory.

Correct usage example:

df_result = df2.join(broadcast(df1), 'id')

This is a map-side join, where each executor joins its local partition of the large dataset with the broadcasted copy of the small one.

Why the other options are incorrect:

A: Broadcasting does not change partition sizes.

B: Joins always match on key equality; this is not specific to broadcast joins.

D: Broadcasting does not filter; it distributes data for faster joins.

Databricks Exam Guide (June 2025): Section ''Developing Apache Spark DataFrame/DataSet API Applications'' --- broadcast joins and partitioning strategies.

PySpark SQL Functions --- broadcast() method documentation.

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The Spark History Server provides a web UI for viewing past completed applications, including event logs, stages, and performance metrics.

If disabled:

Spark jobs still run normally,

But users lose the ability to review historical job metrics, DAGs, or logs after completion.

Thus, debugging, performance analysis, and audit capabilities are lost.

Why the other options are incorrect:

A: Disabling History Server doesn't manage logs.

B/D: Minimal overhead; disabling doesn't improve runtime speed or executor performance.

Databricks Exam Guide (June 2025): Section ''Apache Spark Architecture and Components'' --- Spark UI, History Server, and event logging.

Spark Administration Docs --- History Server functionality and configuration.

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Both cache() and persist() are Spark DataFrame storage operations that store computed results in memory (and optionally on disk) to speed up subsequent actions on the same DataFrame.

Key difference:

cache() is a shorthand for persist(StorageLevel.MEMORY_AND_DISK).

persist() allows specifying different storage levels, such as MEMORY_ONLY, DISK_ONLY, or MEMORY_AND_DISK_SER.

Example:

df.cache() # Uses MEMORY_AND_DISK by default

df.persist(StorageLevel.MEMORY_ONLY) # Custom storage level

Both trigger caching upon an action (e.g., count(), collect()).

Why the other options are incorrect:

A: persist() default is not DISK_ONLY; default storage level is MEMORY_AND_DISK.

B/C: cache() cannot set arbitrary levels; only persist() can.

PySpark API Reference --- DataFrame.cache() and DataFrame.persist().

Databricks Exam Guide (June 2025): Section ''Developing Apache Spark DataFrame/DataSet API Applications'' --- caching, persistence, and storage levels.

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The correct approach to perform a parallelized groupBy operation across Spark worker nodes using Pandas API is via applyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.

As per the Databricks documentation:

'applyInPandas() allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution.'

Option A is correct and achieves this parallel execution.

Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.

Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.

Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.

Therefore, to run a groupBy with parallel Pandas logic on Spark workers, Option A using applyInPandas is the only correct answer.