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

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

3. 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) append
B) aggregate
C) replace
D) complete

4. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

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

5. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B) Uses trigger() - default micro-batch trigger without interval.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

Solutions: