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

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

2. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
D) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.

3. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The operation will load all data into the driver's memory, potentially causing memory overflow
B) Data will be lost during conversion
C) The conversion will automatically distribute the data across worker nodes
D) The operation will fail if the Pandas DataFrame exceeds 1000 rows

4. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A) FATAL, NONE, INFO, DEBUG
B) WARN, NONE, ERROR, FATAL
C) ERROR, WARN, TRACE, OFF
D) ALL, DEBUG, FAIL, INFO

5. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the sortBy() transformation to reorganize the data
B) Use the coalesce() transformation with a lower number of partitions
C) Use the repartition() transformation with a lower number of partitions
D) Use the distinct() transformation to combine similar partitions

Solutions: