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

1. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(continuous=True)
B) .trigger(processingTime='1 second')
C) .trigger(availableNow=True)
D) .trigger(continuous='1 second')

2. 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', schemaMerge
'true')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')

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

4. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Recreate the users_vw and query the data using Spark
B) Save the users_vw definition and query using Spark
C) Persist the users_vw data as a table
D) Query the users_vw using Spark

5. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the sortBy() transformation to reorganize the data
B) Use the coalesce() transformation with a lower number of partitions
C) Use the distinct() transformation to combine similar partitions
D) Use the repartition() transformation with a lower number of partitions

Solutions: