Databricks Certified Associate Developer for Apache Spark 3.5 Exam - Topic 4 Question 2 Discussion

Actual exam question for Databricks's Databricks Certified Associate Developer for Apache Spark 3.5 exam

Question #: 2
Topic #: 4

[All Databricks Certified Associate Developer for Apache Spark 3.5 Questions]

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?

AIt removes all duplicates regardless of when they arrive.

BIt accepts watermarks in seconds and the code results in an error.

CIt removes duplicates that arrive within the 30-minute window specified by the watermark.

DIt is not able to handle deduplication in this scenario.

Show Suggested Answer

Suggested Answer: C

In Structured Streaming, a watermark defines the maximum delay for event-time data to be considered in stateful operations like deduplication or window aggregations.

Behavior:

df = df.withWatermark('event_timestamp', '30 minutes')

This sets a 30-minute watermark, meaning Spark will only keep track of events that arrive within 30 minutes of the latest event time seen so far. When used with:

df.dropDuplicates(['event_id', 'event_timestamp'])

Spark removes duplicates that arrive within the watermark threshold (in this case, within 30 minutes).

Why other options are incorrect:

A: Watermarks do not remove all duplicates; they only manage those within the defined event-time window.

B: Watermark durations can be expressed as strings like '30 minutes', '10 seconds', etc., not only seconds.

D: Structured Streaming supports deduplication using withWatermark() and dropDuplicates().

Reference (Databricks Apache Spark 3.5 -- Python / Study Guide):

PySpark Structured Streaming Guide --- withWatermark() and dropDuplicates() methods for event-time deduplication.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section ''Structured Streaming'' --- Topic: Streaming Deduplication with and without watermark usage.

by Leonard at Nov 19, 2025, 12:10 PM

Limited Time Offer

25%

Off

Get Premium Databricks Certified Associate Developer for Apache Spark 3.5 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Tandra

9 hours ago

I thought watermarks only help with late data, not deduplication.

upvoted 0 times

...

Tarra

6 days ago

Wait, does it really handle all duplicates?

upvoted 0 times

...

Lou

11 days ago

Totally agree, that's how watermarks work!

upvoted 0 times

...

Gayla

16 days ago

It removes duplicates within the 30-minute window.

upvoted 0 times

...

Noah

21 days ago

Haha, I bet the data engineer is regretting not using a unique identifier instead of relying on timestamps.

upvoted 0 times

...

Laine

26 days ago

D) I don't think the code can handle this scenario. Deduplication is tricky with streaming data.

upvoted 0 times

...

Sina

1 month ago

A) Removing all duplicates regardless of arrival time sounds too good to be true.

upvoted 0 times

...

Chandra

1 month ago

C) Seems like the right answer. The watermark will help remove duplicates within the 30-minute window.

upvoted 0 times

...

Sabra

1 month ago

I'm a bit confused about how watermarks work in practice. I thought they just marked late data, not necessarily removed duplicates. Maybe it's option D?

upvoted 0 times

...

Joanne

2 months ago

If I recall correctly, the watermark should help with deduplication within that 30-minute window, so I think option C makes sense.

upvoted 0 times

...

Ernie

2 months ago

I think the watermark only affects how late data is processed, so it might not remove all duplicates. I feel like I've seen a similar question where it specified a time window.

upvoted 0 times

...

Laine

2 months ago

I'm not too sure about this one. The question mentions that the engineer is trying to remove duplicates, but the watermark function is typically used for handling late-arriving records. I'm not convinced that C is the right answer, and I'm a bit worried that D might be the case here.

upvoted 0 times

...

Harrison

2 months ago

I remember something about watermarks being used to handle late data, but I'm not sure if it actually removes duplicates or just helps manage them.

upvoted 0 times

...

Penney

2 months ago

The key here is that the duplicate records have a time difference of at most 30 minutes in the event_timestamp column. So the watermark function should be able to handle this case and remove the duplicates that arrive within that window. I'm pretty confident C is the correct answer.

upvoted 0 times

...

Chantay

2 months ago

I think it’s C. It makes sense to deduplicate within the 30-minute window.

upvoted 0 times

...

Agustin

3 months ago

No way it removes all duplicates, that's not how it works!

upvoted 0 times

...

Harrison

3 months ago

C) The watermark should work to remove duplicates within the specified time window.

upvoted 0 times

...

Dalene

3 months ago

Hmm, I'm a bit confused. I thought the watermark was used to handle out-of-order data, not necessarily duplicates. I'm not sure if this is the right approach for deduplication.

upvoted 0 times

...

Robt

3 months ago

I think the answer is C. The watermark function is used to handle late-arriving records, and the 30-minute window should remove duplicates that arrive within that time frame.

upvoted 0 times

...