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

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

1. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) Data will be lost during conversion
D) The operation will load all data into the driver's memory, potentially causing memory overflow

2. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
B) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
C) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
D) spark.read.parquet("/data/parquet/")

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

4. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) employees_df.filter(employees_df.tenure >= 5).show()
B) filter(employees_df.tenure >= 5)
C) employees_df.filter(employees_df.tenure >= 5).collect()
D) employees_df.where(employees_df.tenure >= 5)

5. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
B) It computes results immediately using eager execution.
C) It runs on a single node only, utilizing memory efficiently.
D) It is available only with Python, thereby reducing the learning curve.

Solutions: