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  1. Home
  2. Databricks Certification
  3. Associate-Developer-Apache-Spark-3.5 Exam
  4. Databricks.Associate-Developer-Apache-Spark-3.5.v2025-11-20.q72 Dumps
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Question 41

Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

Correct Answer: D
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API
insert code

Question 42

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?

Correct Answer: D
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
insert code

Question 43

12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

Correct Answer: C
In Spark's DataFrame API, the dropna() (or equivalently, DataFrameNaFunctions.drop()) method removes rows containing null values.
Behavior:
how="any" → drops rows where any column has a null value.
how="all" → drops rows where all columns are null.
Since the data scientist wants to drop records with any null field, the correct parameter is how="any".
Correct syntax:
filtered_users = raw_users.dropna(how="any")
This will remove all records that have at least one null value in any column.
Why the other options are incorrect:
A: Uses na.drop("any") but missing parentheses context (works only as raw_users.na.drop("any"), which is equivalent to option C).
B/D: how="all" only removes rows where all values are null - too strict for this use case.
Reference:
PySpark DataFrame API - DataFrameNaFunctions.drop() and DataFrame.dropna().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - covers handling missing data and DataFrame cleaning operations.
insert code

Question 44

43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

Correct Answer: C
The Spark History Server provides a web UI for viewing past completed applications, including event logs, stages, and performance metrics.
If disabled:
Spark jobs still run normally,
But users lose the ability to review historical job metrics, DAGs, or logs after completion.
Thus, debugging, performance analysis, and audit capabilities are lost.
Why the other options are incorrect:
A: Disabling History Server doesn't manage logs.
B/D: Minimal overhead; disabling doesn't improve runtime speed or executor performance.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - Spark UI, History Server, and event logging.
Spark Administration Docs - History Server functionality and configuration.
insert code

Question 45

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?

Correct Answer: C
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.
insert code
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