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

1. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write.saveAsTable("default_table").option("path", "/some/path")
B) users.write.saveAsTable("default_table", path="/some/path")
C) users.write.option("path", "/some/path").saveAsTable("default_table")
D) users.write(path="/some/path").saveAsTable("default_table")

2. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Configure the Spark UI to automatically collect maximum times.
B) Broadcast a variable to share the maximum time among workers.
C) Use an RDD action like reduce() to compute the maximum time.
D) Use an accumulator to record the maximum time on the driver.

3. 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 it must use on='employee_id' to specify the join column explicitly
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 PySpark does not support joining DataFrames with a different structure

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

5. 42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts")) \
.withColumn("event_month", F.month("event_ts")) \
.bucketBy(42, ["event_year", "event_month"]) \
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?

A) Replace .bucketBy() with .partitionBy("event_year") only
B) Replace .bucketBy() with .partitionBy("event_year", "event_month")
C) Change the bucket count (42) to a lower number
D) Add .sortBy() after .bucketBy()

Solutions: