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

1. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/*")
C) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
D) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")

2. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
B) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
C) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
D) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()

3. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
B) Set the number of partitions equal to the total number of CPUs in the cluster
C) Set the number of partitions equal to the number of nodes in the cluster
D) Set the number of partitions to a fixed value, such as 200

4. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(continuous=True)
B) .trigger(availableNow=True)
C) .trigger(continuous='1 second')
D) .trigger(processingTime='1 second')

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 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

Solutions: