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

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

2. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

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

3. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
B) dates_df.withColumn("date", f.to_date("timestamp")).show()
C) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
D) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()

4. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) filter(employees_df.tenure >= 5)
B) employees_df.filter(employees_df.tenure >= 5).collect()
C) employees_df.where(employees_df.tenure >= 5)
D) employees_df.filter(employees_df.tenure >= 5).show()

5. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
B) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
C) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
D) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

Solutions: