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

1. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

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

2. 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='1 second')
B) .trigger(processingTime='1 second')
C) .trigger(continuous=True)
D) .trigger(availableNow=True)

3. A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A) It removes duplicates that arrive within the 30-minute window specified by the watermark
B) It removes all duplicates regardless of when they arrive
C) It accepts watermarks in seconds and the code results in an error
D) It is not able to handle deduplication in this scenario

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

5. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Dynamically switching join strategies
B) Improving the performance of single-stage Spark jobs
C) Optimizing the layout of Delta files on disk
D) Collecting persistent table statistics and storing them in the metastore for future use

Solutions: