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

1. A data engineer wants to create an external table from a JSON file located at/data/input.jsonwith the following requirements:
Create an external table namedusers
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

A) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema
'true')
B) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
'true')

2. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(how='any')
B) filtered_df = users_raw_df.na.drop(thresh=0)
C) filtered_df = users_raw_df.na.drop(how='all')
D) filtered_df = users_raw_df.na.drop(how='all', thresh=None)

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

4. Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

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

5. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A) Broadcast a variable to share the maximum time among workers
B) Configure the Spark UI to automatically collect maximum times
C) Use an accumulator to record the maximum time on the driver
D) Use an RDD action like reduce() to compute the maximum time

Solutions: