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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q24-Q29):

NEW QUESTION # 24
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
B. Uses trigger() - default micro-batch trigger without interval.
C. Uses trigger(continuous='5 seconds') - continuous processing mode.
D. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

Answer: A

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers

NEW QUESTION # 25
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_spark()
B. psdf.to_pyspark()
C. psdf.to_pandas()
D. psdf.to_dataframe()

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

NEW QUESTION # 26
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: int) -> int:
return t * 3.14159
B. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
C. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

Answer: C

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 # 27
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

A. df = df.filter(F.col("transaction_id").isNotNull())
B. df = df.dropDuplicates()
C. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
D. df = df.dropDuplicates(["transaction_amount"])

Answer: B

Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API

NEW QUESTION # 28
A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

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

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource- constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final Answer: C

NEW QUESTION # 29
......

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