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

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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. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = regions.select("region_id", "region_name").take(3)
B) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
C) regions_dict = dict(regions.take(3))
D) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())

2. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) coalesce
B) filter
C) select
D) groupBy

3. A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns for transaction_id, account_number, transaction_amount, and timestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

A) df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first("timestamp"))
B) df = df.filter(F.col("transaction_id").isNotNull())
C) df = df.dropDuplicates(["transaction_amount"])
D) df = df.dropDuplicates()

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

5. 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.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
D) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

Solutions: