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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", from_utc_timestamp(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", date_format(col("transaction_datetime"), "America/New_York"))

2. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It runs on a single node only, utilizing memory efficiently.
B) It computes results immediately using eager execution.
C) It is available only with Python, thereby reducing the learning curve.
D) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

3. 28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) coalesce
B) groupBy
C) select
D) filter

4. An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A) The code fails to execute because the column names employee_id and emp_id do not match automatically
B) The code fails to execute because PySpark does not support joining DataFrames with a different structure
C) The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
D) The code fails to execute because it must use on='employee_id' to specify the join column explicitly

5. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Improving the performance of single-stage Spark jobs
B) Dynamically switching join strategies
C) Collecting persistent table statistics and storing them in the metastore for future use
D) Optimizing the layout of Delta files on disk

Solutions: