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

1. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Ability to process small datasets efficiently
B) In-memory computation and parallel processing capabilities
C) Built-in machine learning libraries
D) Support for SQL queries on structured data

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) -> str:
C) def shake_256(raw: str) -> str:
D) def shake_256(df: pd.Series) -> pd.Series:

3. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Use spark.read.json() with the inferSchema option set to true
B) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
C) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
D) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

4. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It can be used to interact with any remote cluster using the REST API
B) It provides a way to run Spark applications remotely in any programming language
C) It is primarily used for data ingestion into Spark from external sources
D) It allows for remote execution of Spark jobs

5. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.

Solutions: