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

When two DataFrames have different column sets, the normal union() or unionAll() functions fail unless both have exactly the same columns in the same order.

Solution: Use unionByName() with allowMissingColumns=True.

This aligns columns by name and automatically adds missing columns with null values.

Correct syntax:

combined_df = au_df.unionByName(nz_df, allowMissingColumns=True)

This ensures the union works even if one DataFrame has extra or missing columns.

Why the other options are incorrect:

B: unionAll() is deprecated; also requires identical schemas.

C: With allowMissingColumns=False, Spark still throws a mismatch error.

D: union() doesn’t accept the allowMissingColumns argument.

In Apache Spark's cluster mode:

"The driver program runs on the cluster’s worker node instead of the client’s local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs."

(Source: Apache Spark documentation – Cluster Mode Overview)

This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.

Option A is partially true but less specific than D.

Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.

Option C describes client mode, not cluster mode.

The number of tasks is controlled by the number of partitions. By default, spark.sql.shuffle.partitions is 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.

From the Spark tuning guide:

"To improve performance, especially for large clusters, increase spark.sql.shuffle.partitions to create more tasks and parallelism."

Thus:

A is correct: increasing shuffle partitions increases parallelism

B is wrong: it further reduces parallelism

C is invalid: increasing dataset size doesn’t guarantee more partitions

D is irrelevant to task count per stage

Final Answer: A

To filter rows based on a condition and display them in Spark, use filter(...).show():

employees_df.filter(employees_df.tenure >= 5).show()

Option A is correct and shows the results.

Option B filters but doesn’t display them.

Option C uses Python’s built-in filter, not Spark.

Option D collects the results to the driver, which is unnecessary if .show() is sufficient.

Final Answer: A

Watermarking in Structured Streaming defines how late a record can arrive based on event time before Spark discards it.

Behavior:

withWatermark("event_time", "10 minutes")

This means Spark will keep state for 10 minutes beyond the maximum event time seen so far.

Any data arriving later than 10 minutes after the current watermark is ignored — it will not be included in the aggregation or output.

Why the other options are incorrect:

B: Late data beyond the watermark threshold is not included.

C: Late data is not moved to a new window; it’s simply dropped.

D: True for late data within the watermark threshold, not after it.

Spark Connect enables a decoupled client-server architecture, allowing remote clients to run Spark code via gRPC.

However, as of Spark 3.5, Spark Connect supports DataFrame and SQL APIs, but not RDD APIs.

Limitation:

Applications that rely heavily on RDD-based transformations or actions cannot be migrated directly to Spark Connect.

These APIs require tight driver integration, which Spark Connect intentionally decouples.

Thus, complete Spark API compatibility is not yet achieved — this is the key adoption blocker.

Why the other options are incorrect:

A: Debugging is possible through IDE integration and logs on the client side.

B: Spark Connect actually supports upgradable clients independent of the driver — this is an advantage, not a limitation.

D: Spark Connect provides strong isolation between the client and driver processes.

Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.

From Spark documentation:

“groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key.”

Option A (groupBy + agg) → causes shuffle.

Options B, C, and D (filter, withColumn, select) → transformations that do not require shuffling; they are narrow dependencies.

Final Answer: A

A Spark job is triggered by an action (e.g., count, show).

The job is broken into stages, typically one per shuffle boundary.

Each stage is divided into multiple tasks, which are distributed across worker nodes.