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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q55-Q60):

NEW QUESTION # 55
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 ofshake_256_udfto this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of theshake_256()function be changed to in order to fix this error?

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

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When converting a standard PySpark UDF to a Pandas UDF for performance optimization, the function must operate on a Pandas Series as input and return a Pandas Series as output.
In this case, the original function signature:
def shake_256(raw: str) -> str
is scalar - not compatible with Pandas UDFs.
According to the official Spark documentation:
"Pandas UDFs operate onpandas.Seriesand returnpandas.Series. The function definition should be:
def my_udf(s: pd.Series) -> pd.Series:
and it must be registered usingpandas_udf(...)."
Therefore, to fix the error:
The function should be updated to:
def shake_256(df: pd.Series) -> pd.Series:
return df.apply(lambda x: hashlib.shake_256(x.encode()).hexdigest(20))
This will allow Spark to efficiently execute the Pandas UDF in vectorized form, improving performance compared to standard UDFs.
Reference: Apache Spark 3.5 Documentation # User-Defined Functions # Pandas UDFs

NEW QUESTION # 56
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 distinct() transformation to combine similar partitions
B. Use the sortBy() transformation to reorganize the data
C. Use the repartition() transformation with a lower number of partitions
D. Use the coalesce() transformation with a lower number of partitions

Answer: D

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

NEW QUESTION # 57
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A. Modify the Spark configuration to disable garbage collection
B. Increase the memory allocated to the Spark Driver.
C. Optimize the data processing logic by repartitioning the DataFrame.
D. Cache large DataFrames to persist them in memory.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.

NEW QUESTION # 58
Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A. When the groupBy transformation is applied
B. When the filter transformation is applied
C. When the count action is applied
D. When the show action is applied

Answer: C

Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation

NEW QUESTION # 59
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.mode("append").partitionBy("country").parquet("/data/output")
B. df.write.mode("overwrite").parquet("/data/output")
C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
D. df.write.partitionBy("country").parquet("/data/output")

Answer: C

Explanation:
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide

NEW QUESTION # 60
......

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NEW ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 BRAINDUMPS PDF & ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 UPDATED DEMO

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