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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

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

2. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A job contains multiple tasks, and each task contains multiple stages.
B) A job contains multiple stages, and each stage contains multiple tasks.
C) A stage contains multiple jobs, and each job contains multiple tasks.
D) A stage contains multiple tasks, and each task contains multiple jobs.

3. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It provides a way to run Spark applications remotely in any programming language
B) It is primarily used for data ingestion into Spark from external sources
C) It can be used to interact with any remote cluster using the REST API
D) It allows for remote execution of Spark jobs

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

5. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").orderBy(df["Name"].asc())
B) df.select("Name").distinct().orderBy(df["Name"])
C) df.select("Name").distinct().orderBy(df["Name"].desc())
D) df.select("Name").distinct()

Solutions: