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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. 16 of 55.
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) Only actions trigger the execution of the transformation pipeline.
C) The Spark engine requires manual intervention to start executing transformations.
D) Transformations are evaluated lazily.
E) Transformations are executed immediately to build the lineage graph.

2. What is the benefit of using Pandas on Spark for data transformations?
Options:

A) It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost-efficient.
B) It computes results immediately using eager execution, making it simple to use.
C) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D) It is available only with Python, thereby reducing the learning curve.

3. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be added to the start date.
B) The same start date will be returned.
C) An error message of an invalid parameter will be returned.
D) The number of days specified will be removed from the start date.

4. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

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

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

Solutions: