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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. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

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

2. 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) Improved job execution speed due to reduced logging overhead
B) Enhanced executor performance due to reduced log size
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

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

4. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) df.select(length(col("stringColumn")).alias("length"))
B) spark.udf.register("stringLength", lambda s: len(s))
C) df.withColumn("length", udf(lambda s: len(s), StringType()))
D) df.withColumn("length", spark.udf("len", StringType()))

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

Solutions: