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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A broadcast nested loop join
B) A sort-merge join
C) A shuffled hash join
D) A Cartesian join

2. A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A) It removes all duplicates regardless of when they arrive
B) It accepts watermarks in seconds and the code results in an error
C) It is not able to handle deduplication in this scenario
D) It removes duplicates that arrive within the 30-minute window specified by the watermark

3. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

A) It optimizes by reading only the relevant subset of data from fewer partitions.
B) It ensures that data is loaded into memory all at once for faster query execution.
C) It compresses the data to save disk space.
D) It automatically cleans up unused partitions to optimize storage.

4. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
B) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
C) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
D) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.sql.shuffle.partitions
B) spark.task.maxFailures
C) spark.executor.cores
D) spark.executor.memory

Solutions: