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

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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. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.executor.memory
B) spark.executor.cores
C) spark.task.maxFailures
D) spark.driver.cores

2. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')

3. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
B) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
C) It processes data solely on disk storage, reducing the need for memory resources.
D) It eliminates the need to write any code, automatically handling all data processing.
E) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.

4. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Save the users_vw definition and query using Spark
B) Persist the users_vw data as a table
C) Recreate the users_vw and query the data using Spark
D) Query the users_vw using Spark

5. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) The Spark engine optimizes the execution plan during the transformations, causing delays.
B) Transformations are evaluated lazily.
C) Transformations are executed immediately to build the lineage graph.
D) The Spark engine requires manual intervention to start executing transformations.
E) Only actions trigger the execution of the transformation pipeline.

Solutions: