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

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

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

2. Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

A) The driver node stores the final result after computations are completed by worker nodes
B) The driver node only provides the user interface for monitoring the application
C) The driver node holds the DataFrame data and performs all computations locally
D) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes

3. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

4. 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:

5. A developer runs:

What is the result?
Options:

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

Solutions: