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

1. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) aggregate
B) replace
C) complete
D) append

2. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions to a fixed value, such as 200
B) Set the number of partitions equal to the number of nodes in the cluster
C) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
D) Set the number of partitions equal to the total number of CPUs in the cluster

3. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
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')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
C) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')

4. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Transformations are evaluated lazily.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) Only actions trigger the execution of the transformation pipeline.
D) The Spark engine requires manual intervention to start executing transformations.
E) Transformations are executed immediately to build the lineage graph.

5. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the number of executor instances to handle more concurrent tasks.
B) Increase the executor memory allocation in the Spark configuration.
C) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
D) Reduce the size of the data partitions to improve task scheduling.

Solutions: