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

The approx_percentile function in Spark is a performance-optimized alternative to percentile. It takes an optional accuracy parameter:

approx_percentile(column, percentage, accuracy)

Higher accuracy values → more precise results, but increased memory/computation.

Lower values → faster but less accurate.

From the documentation:

“Increasing the accuracy improves precision but increases memory usage.”

Final Answer: D

Apache Spark follows a lazy evaluation model, meaning transformations (like filter(), select(), map()) are not executed immediately. Instead, they build a logical plan (lineage graph) that represents the sequence of operations to be applied.

Execution only begins when an action (e.g., count(), collect(), save(), show()) is called. At that point, Spark’s engine:

Optimizes the logical plan into a physical plan.

Divides it into stages and tasks.

Executes them across the cluster.

This design helps Spark optimize execution paths and avoid unnecessary computations.

Why the other options are incorrect:

A: Transformations do not execute immediately; they are deferred.

B: Optimization happens during job execution (after an action), not during transformations.

D: Execution starts automatically once an action is triggered, no manual intervention needed.

f.to_date() converts a timestamp or string to a DateType.

Ideal for extracting the date component (year-month-day) from a full timestamp.

Example:

from pyspark.sql.functions import to_date

dates_df.withColumn("date", to_date("timestamp"))

To extract unique values from a column and sort them alphabetically:

distinct() is required to remove duplicate values.

orderBy() is needed to sort the results alphabetically (ascending by default).

Correct code:

df.select("Name").distinct().orderBy(df["Name"])

This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting. Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

Spark automatically uses a broadcast hash join when one side of the join is small enough to fit within the broadcast threshold.

Default threshold:

spark.sql.autoBroadcastJoinThreshold = 10MB (as of Spark 3.5)

Since df2 is 8 MB, Spark automatically broadcasts it to all executors. This avoids a shuffle on the large dataset (df1) and speeds up the join.

Why the other options are incorrect:

A: 8 MB < 10 MB threshold → broadcast join is efficient.

B: AQE is not required for broadcast joins; it’s a static optimization.

C: Broadcast hints are optional — Spark infers automatically.