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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q23-Q28):

NEW QUESTION # 23
A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers","host1:port1,host2:port2")
.[LINE5]
.load()
Options:

A. .option("subscribe.topic", "feed")
B. .option("subscribe", "feed")
C. .option("kafka.topic", "feed")
D. .option("topic", "feed")

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
To read from a specific Kafka topic using Structured Streaming, the correct syntax is:
python
CopyEdit
option("subscribe","feed")
This is explicitly defined in the Spark documentation:
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source:Apache Spark Structured Streaming + Kafka Integration Guide)
B)."subscribe.topic" is invalid.
C)."kafka.topic" is not a recognized option.
D)."topic" is not valid for Kafka source in Spark.

NEW QUESTION # 24
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A. users.write.saveAsTable("default_table").option("path", "/some/path")
B. users.write.option("path", "/some/path").saveAsTable("default_table")
C. users.write.saveAsTable("default_table", path="/some/path")
D. users.write(path="/some/path").saveAsTable("default_table")

Answer: B

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table

NEW QUESTION # 25
In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A. complete
B. replace
C. aggregate
D. append

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is"complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
Reference: Spark Structured Streaming Programming Guide # Output Modes

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: 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 # 27
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.
From the Spark 3.5 documentation on structured streaming:
"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive." (Source:Structured Streaming Programming Guide) In optionD, the use of:
python
CopyEdit
groupBy("sensor_id", window("timestamp","5 minutes"))
agg(avg("temperature").alias("avg_temp"))
ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.
Explanation of why other options are incorrect:
Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.
Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.
Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.
Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.

NEW QUESTION # 28
......

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