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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. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and 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.withColumn("discount", df.purchase_amount * 0.1).select("discount")
B) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
C) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)

2. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option recoveryLocation during the SparkSession initialization
B) By configuring the option recoveryLocation during writeStream
C) By configuring the option checkpointLocation during readStream
D) By configuring the option checkpointLocation during writeStream

3. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
B) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

4. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Collecting persistent table statistics and storing them in the metastore for future use
B) Dynamically switching join strategies
C) Optimizing the layout of Delta files on disk
D) Improving the performance of single-stage Spark jobs

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

Solutions: