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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q43-Q48):

NEW QUESTION # 43
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A. The driver node holds the DataFrame data and performs all computations locally
B. The driver node only provides the user interface for monitoring the application
C. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
D. The driver node stores the final result after computations are completed by worker nodes

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.

NEW QUESTION # 44
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

A. The number of days specified will be added to the start date
B. The same start date will be returned
C. The number of days specified will be removed from the start date
D. An error message of an invalid parameter will be returned

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.
Reference: Spark SQL Functions # date_sub()

NEW QUESTION # 45
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
B. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
C. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
D. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.

NEW QUESTION # 46
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A. Only actions trigger the execution of the transformation pipeline.
B. The Spark engine requires manual intervention to start executing transformations.
C. The Spark engine optimizes the execution plan during the transformations, causing delays.
D. Transformations are evaluated lazily.
E. Transformations are executed immediately to build the lineage graph.

Answer: A,D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

NEW QUESTION # 47
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. Optimize the data processing logic by repartitioning the DataFrame.
B. Cache large DataFrames to persist them in memory.
C. Modify the Spark configuration to disable garbage collection
D. Increase the memory allocated to the Spark Driver.

Answer: D

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 # 48
......

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q43-Q48):

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