Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam With Confidence Using Practice Dumps

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

In Structured Streaming, a watermark defines the maximum delay for event-time data to be considered in stateful operations like deduplication or window aggregations.

Behavior:

df = df.withWatermark("event_timestamp", "30 minutes")

This sets a 30-minute watermark, meaning Spark will only keep track of events that arrive within 30 minutes of the latest event time seen so far. When used with:

df.dropDuplicates(["event_id", "event_timestamp"])

Spark removes duplicates that arrive within the watermark threshold (in this case, within 30 minutes).

Why other options are incorrect:

A: Watermarks do not remove all duplicates; they only manage those within the defined event-time window.

B: Watermark durations can be expressed as strings like "30 minutes", "10 seconds", etc., not only seconds.

D: Structured Streaming supports deduplication using withWatermark() and dropDuplicates().

References (Databricks Apache Spark 3.5 – Python / Study Guide):

PySpark Structured Streaming Guide — withWatermark() and dropDuplicates() methods for event-time deduplication.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section “Structured Streaming” — Topic: Streaming Deduplication with and without watermark usage.

The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language model is created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.

To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.

From the official Databricks documentation:

“Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive… For example, loading a ML model once per executor rather than once per row/batch.”

— Databricks Official Docs: Pandas UDFs

Correct implementation looks like:

python

CopyEdit

@pandas_udf("string")

def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:

model = get_translation_model(target_lang='es')

for batch in batch_iter:

yield batch.apply(model)

This refactor ensures the get_translation_model() is invoked once per executor process, not per batch, significantly improving pipeline performance.