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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. 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.

2. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()

3. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster
B) Switch the deployment mode to cluster mode
C) Increase the driver memory on the client machine
D) Switch the deployment mode to local mode

4. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.format("json").save("path/to/file", mode="overwrite")
B) df.write.overwrite.json("path/to/file")
C) df.write.mode("overwrite").json("path/to/file")
D) df.write.json("path/to/file", overwrite=True)

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

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

Solutions: