Prepare with Databricks Associate-Developer-Apache-Spark-3.5 exam practice material, pass for sure

Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:

Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Avoid using a SparkSession and rely on SparkContext only.
B) Create a new SparkSession instance before each transformation.
C) Use a single SparkSession instance for the entire application.
D) Stop and restart the SparkSession after each action.

2. 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 of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_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) -> pd.Series:
C) def shake_256(raw: str) -> str:
D) def shake_256(df: pd.Series) -> str:

3. A developer runs:

What is the result?
Options:

A) It stores all data in a single Parquet file.
B) It creates separate directories for each unique combination of color and fruit.
C) It throws an error if there are null values in either partition column.
D) It appends new partitions to an existing Parquet file.

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

5. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Configure the application to run in cluster mode instead of local mode.
B) Set the spark.executor.memory property to a large value.
C) Increase the number of local threads based on the number of CPU cores.
D) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.

Solutions: