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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q51-Q56):

NEW QUESTION # 51
A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.start()
B. query = streaming_df.writeStream
.format("console")
.outputMode("append")
.option("checkpointLocation", "/path/to/checkpoint")
.start()
C. query = streaming_df.writeStream
.format("console")
.outputMode("complete")
.start()
D. query = streaming_df.writeStream
.format("console")
.option("checkpoint", "/path/to/checkpoint")
.outputMode("append")
.start()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable fault tolerance and ensure that Spark can resume from the last committed offset after failure, you must configure a checkpoint location using the correct option key:"checkpointLocation".
From the official Spark Structured Streaming guide:
"To make a streaming query fault-tolerant and recoverable, a checkpoint directory must be specified using.
option("checkpointLocation", "/path/to/dir")."
Explanation of options:
Option A uses an invalid option name:"checkpoint"(should be"checkpointLocation") Option B is correct: it setscheckpointLocationproperly Option C lacks checkpointing and won't resume after failure Option D also lacks checkpointing configuration Reference: Apache Spark 3.5 Documentation # Structured Streaming # Fault Tolerance Semantics

NEW QUESTION # 52
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.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
C. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
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 # 53
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
B. spark.conf.set("spark.pandas.arrow.enabled", "true")
C. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
D. spark.conf.set("spark.sql.execution.arrow.enabled", "true")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
From the official documentation:
"This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow." Option B is correct.
Options A, C, and D are invalid config keys and not recognized by Spark.
Final Answer: B

NEW QUESTION # 54
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

B)

C)

D)

A. result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))
B. result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
C. result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))
D. result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

NEW QUESTION # 55
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It filters the id values before performing the join.
B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C. It ensures that the join happens only when the id values are identical.
D. It increases the partition size for df1 and df2.

Answer: B

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins

NEW QUESTION # 56
......

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