ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 VALID GUIDE FILES, TEST ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 SIMULATOR ONLINE

Associate-Developer-Apache-Spark-3.5 Valid Guide Files, Test Associate-Developer-Apache-Spark-3.5 Simulator Online

Associate-Developer-Apache-Spark-3.5 Valid Guide Files, Test Associate-Developer-Apache-Spark-3.5 Simulator Online

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q82-Q87):

NEW QUESTION # 82
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: pd.Series) -> str:
  • B. def shake_256(raw: str) -> str:
  • C. def shake_256(df: pd.Series) -> pd.Series:
  • D. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

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 # 83
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 count action is applied
  • B. When the groupBy transformation is applied
  • C. When the show action is applied
  • D. When the filter transformation is applied

Answer: A

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 # 84
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("topic", "feed")
  • B. .option("kafka.topic", "feed")
  • C. .option("subscribe", "feed")
  • D. .option("subscribe.topic", "feed")

Answer: C

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 # 85
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

  • A. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
  • B. cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
  • C. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.
  • D. persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)
df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.
Reference:Spark Programming Guide - Caching and Persistence


NEW QUESTION # 86
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

  • A. save with mode Ignore
  • B. save with mode ErrorIfExists
  • C. saveAsTable with mode Overwrite
  • D. saveAsTable with mode ErrorIfExists

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A


NEW QUESTION # 87
......

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