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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. 33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A) raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
B) raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
C) raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
D) raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))

2. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
B) Increase the number of local threads based on the number of CPU cores.
C) Set the spark.executor.memory property to a large value.
D) Configure the application to run in cluster mode instead of local mode.

3. 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.

4. 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.

5. 26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.remove("name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("name", "email", "birthdate")
C) df_user_non_pii = df_user.select("name", "email", "birthdate")
D) df_user_non_pii = df_user.dropFields("name", "email", "birthdate")

Solutions: