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

1. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the size of the dataset to create more partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Increase the value of spark.sql.shuffle.partitions
D) Reduce the value of spark.sql.shuffle.partitions

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

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

3. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.task.maxFailures
B) spark.executor.memory
C) spark.driver.cores
D) spark.executor.cores

4. A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

A) df.select("Name").distinct().orderBy(df["Name"].desc())
B) df.select("Name").orderBy(df["Name"].asc())
C) df.select("Name").distinct().orderBy(df["Name"])
D) df.select("Name").distinct()

5. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

D)

A) Use theapplyInPandasAPI:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
B) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
C) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
D) Use themapInPandasAPI:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

Solutions: