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

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

2. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
B) Configure the application to run in cluster mode instead of local mode.
C) Increase the number of local threads based on the number of CPU cores.
D) Set the spark.executor.memory property to a large value.

3. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to cluster mode.
B) Add more executor instances to the cluster.
C) Increase the driver memory on the client machine.
D) Switch the deployment mode to local mode.

4. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.orderBy(col("count").desc(), col("Name").asc())
B) df1.orderBy(col("Name").desc(), col("count").asc())
C) df1.sort("Name", "count")
D) df1.orderBy("Name", "count")

5. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Improving the performance of single-stage Spark jobs
D) Dynamically switching join strategies

Solutions: