VALID CERTIFICATION ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM INFOR - AUTHORITATIVE SOURCE OF ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM

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Valid Certification Associate-Developer-Apache-Spark-3.5 Exam Infor - Authoritative Source of Associate-Developer-Apache-Spark-3.5 Exam

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q14-Q19):

NEW QUESTION # 14
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?

  • A. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:
  • B. def shake_256(df: pd.Series) -> str:
  • C. def shake_256(df: pd.Series) -> pd.Series:
  • D. def shake_256(raw: str) -> str:

Answer: C

Explanation:
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


NEW QUESTION # 15
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

  • A. DataFrame.filter()
  • B. DataFrame.withColumn()
  • C. DataFrame.groupBy().agg()
  • D. DataFrame.select()

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A


NEW QUESTION # 16
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 # 17
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

  • A. .trigger(continuous='1 second')
  • B. .trigger(processingTime='1 second')
  • C. .trigger(continuous=True)
  • D. .trigger(availableNow=True)

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.
Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault- tolerance.
trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.
So:
Option A uses micro-batching with a tight trigger interval # minimal latency + exactly-once guarantee.
Final Answer: A


NEW QUESTION # 18
Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

  • A. When the show action is applied
  • B. When the groupBy transformation is applied
  • C. When the count action is applied
  • D. When the filter transformation is applied

Answer: C

Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation


NEW QUESTION # 19
......

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