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

1. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Executors
B) Driver Nodes
C) CPU Cores
D) Worker Nodes

2. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
B) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)

3. A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?

A) def shake_256(df: pd.Series) -> pd.Series:
B) def shake_256(raw: str) -> str:
C) def shake_256(df: pd.Series) -> str:
D) def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

4. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
C) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.start()

5. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Use the command spark-submit with the -verbose flag to print the logs to the console.
B) Fetch the logs by running a Spark job with the spark-sql CLI tool.
C) Locate the executor logs on the Spark master node, typically under the /tmp directory.
D) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.

Solutions: