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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:

Question 1

Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A. groupBy without aggregation (invalid use)
B. dropDuplicates on the exact matching fields
C. dropDuplicates on all columns (wrong criteria)
D. dropDuplicates with no arguments (removes based on all columns)

Question 2

5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A. A stage contains multiple tasks, and each task contains multiple jobs.
B. A job contains multiple stages, and each stage contains multiple tasks.
C. A job contains multiple tasks, and each task contains multiple stages.
D. A stage contains multiple jobs, and each job contains multiple tasks.

Question 3

Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
B. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
C. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
D. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.

Question 4

43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A. Enhanced executor performance due to reduced log size
B. Improved job execution speed due to reduced logging overhead
C. Loss of access to past job logs and reduced debugging capability for completed jobs
D. Prevention of driver log accumulation during long-running jobs

Question 5

A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
C. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

Solutions: