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

1. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A stage contains multiple jobs, and each job contains multiple tasks.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A stage contains multiple tasks, and each task contains multiple jobs.

2. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()

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 ofshake_256_udfto this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of theshake_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:

4. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.overwrite.json("path/to/file")
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.format("json").save("path/to/file", mode="overwrite")
D) df.write.json("path/to/file", overwrite=True)

5. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
C) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
D) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

Solutions: