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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 behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be removed from the start date
B) The number of days specified will be added to the start date
C) The same start date will be returned
D) An error message of an invalid parameter will be returned

2. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
C) query = df.writeStream \
.outputMode("append") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()

3. 19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
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(raw: str) -> str:
C) def shake_256(raw: pd.Series) -> pd.Series:
D) def shake_256(raw: [pd.Series]) -> pd.Series:

4. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
B) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
C) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
D) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.

5. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_spark()
B) pdf.spark()
C) pdf.to_pandas()
D) pdf.to_dataframe()

Solutions: