Databricks Certified Associate Developer for Apache Spark 3.5 Exam - Topic 2 Question 3 Discussion

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

Question #: 3
Topic #: 2

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

2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

Adf.write.json('path/to/file')

Bdf.write.mode('append').json('path/to/file')

Cdf.write.option('overwrite').json('path/to/file')

Ddf.write.mode('overwrite').json('path/to/file')

Show Suggested Answer

Suggested Answer: D

When writing DataFrames to files using the Spark DataFrameWriter API, Spark by default raises an error if the target path already exists. To explicitly overwrite existing data, you must specify the write mode as 'overwrite'.

Correct Syntax:

df.write.mode('overwrite').json('path/to/file')

This command removes the existing file or directory at the specified path and writes the new output in JSON format.

Other supported save modes include:

'append' --- Adds new data to existing files.

'ignore' --- Skips writing if the path already exists.

'error' or 'errorifexists' --- Fails the job if the output path exists (default).

Why other options are incorrect:

A: Defaults to 'error' mode, which fails if the path exists.

B: 'append' only adds data; it does not overwrite existing data.

C: .option('overwrite') is invalid --- mode('overwrite') must be used instead.

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

PySpark API Reference: DataFrameWriter.mode() --- describes valid write modes including 'overwrite'.

PySpark API Reference: DataFrameWriter.json() --- method to write DataFrames in JSON format.

Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section ''Using Spark DataFrame APIs'' --- Reading and writing DataFrames using save modes, schema management, and partitioning.

===========

by Jestine at Nov 21, 2025, 11:20 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

Belen

1 month ago

I feel like D is the standard way to do it.

upvoted 0 times

...

Nell

2 months ago

D is definitely the best choice. Clear and concise.

upvoted 0 times

...

Sunshine

2 months ago

C is a bit confusing. D is more straightforward.

upvoted 0 times

...

Casandra

2 months ago

I'm leaning towards C. Option sounds right too.

upvoted 0 times

...

Elenora

2 months ago

I agree, D makes sense. It explicitly states overwrite.

upvoted 0 times

...

Josue

2 months ago

Yeah, D is the standard way to overwrite files!

upvoted 0 times

...

Domitila

2 months ago

Wait, are you sure? I thought "option" was the key here.

upvoted 0 times

...

Queenie

3 months ago

I thought it was C at first, but D makes more sense.

upvoted 0 times

...

Stevie

3 months ago

Definitely D! That's how you do it.

upvoted 0 times

...

Tora

3 months ago

I'm a bit confused; I thought "option" was the right way to set parameters, but I can't recall if it applies here.

upvoted 0 times

...

Bette

4 months ago

I feel like I've seen "option" used in other contexts, but for overwriting, I think it's definitely about the mode.

upvoted 0 times

...

Laurel

4 months ago

I remember practicing a similar question, and I think option D is correct because it specifies the mode as "overwrite."

upvoted 0 times

...

Emiko

4 months ago

I think the command to overwrite a JSON file is related to the mode, but I'm not entirely sure if it's "overwrite" or "append."

upvoted 0 times

...

Bea

4 months ago

D) df.write.mode("overwrite").json("path/to/file") is the way to go, unless you want to end up with a tangled web of JSON files.

upvoted 0 times

...

Shaniqua

4 months ago

Overwriting JSON files? Sounds like a recipe for a data disaster!

upvoted 0 times

...

Miesha

4 months ago

Hmm, I wonder if the file will be overwritten with my tax returns...

upvoted 0 times

...

Pok

5 months ago

D) df.write.mode("overwrite").json("path/to/file")

upvoted 0 times

...

Mollie

5 months ago

I'm pretty confident that D is the correct answer. The "overwrite" mode is specifically designed for overwriting existing files, so that's the way to go.

upvoted 0 times

...

Tayna

5 months ago

Okay, let me think this through. I know the "mode" option is used to specify whether to append or overwrite the data. So D seems like the right choice here.

upvoted 0 times

...

Verdell

5 months ago

It's D, right? Overwrite mode!

upvoted 0 times

...

Carolynn

5 months ago

C) df.write.option("overwrite").json("path/to/file")

upvoted 0 times

...

Carmela

6 months ago

I think it's D. Overwrite mode is clear.

upvoted 0 times

...

Shawana

6 months ago

I'm a little confused by all the different options here. I'll have to double-check the Spark documentation to make sure I'm choosing the right one.

upvoted 0 times

...

Gaynell

6 months ago

Hmm, I'm a bit unsure about this one. I know there are a few different ways to write a DataFrame to JSON, but I can't quite remember the exact syntax for overwriting an existing file.

upvoted 0 times

...