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Databricks Machine Learning Professional Exam - Topic 9 Question 49 Discussion

A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.Which option best code blocks accomplishes this task?
D) spark.read.table(path).drop(''star_rating'')
E) spark.sql(''SELECT * EXCEPT star_rating FROM path'')
C) Delta tables cannot be modified
B) spark.read.format(''delta'').table(path).drop(''star_rating'')
A) spark.read.format(''delta'').load(path).drop(''star_rating'')

Databricks Machine Learning Professional Exam - Topic 9 Question 49 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 49
Topic #: 9
[All Databricks Machine Learning Professional Questions]

A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.

Which option best code blocks accomplishes this task?

Show Suggested Answer Hide Answer
Suggested Answer: D

by Flo at Jan 26, 2026, 04:01 PM

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Hester
2 hours ago
I feel like option D might miss some steps. Better to be thorough!
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Edelmira
5 days ago
I’m leaning towards option C. It seems efficient for large datasets.
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Jame
10 days ago
I agree, but option A looks good too. It’s straightforward.
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Ty
16 days ago
I think option B is the best choice. It clearly shows how to load and drop the column.
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Arthur
2 months ago
I think you might need to save the changes after dropping the column.
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Kirby
2 months ago
Make sure to load the data first, or it won't work!
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Kiera
2 months ago
Wait, can you really just drop it like that? Seems too simple.
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Isaac
3 months ago
Totally agree, that's the easiest way to do it.
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Matt
3 months ago
Just use the `drop` method on the DataFrame!
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James
3 months ago
Option D is just plain silly. Who would write code like that?
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Bettina
3 months ago
Hmm, I'm torn between B and C. Decisions, decisions!
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Terrilyn
3 months ago
Option A is a bit too verbose, in my opinion.
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Annice
3 months ago
Option C seems the most concise and efficient.
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Lavonna
4 months ago
This looks straightforward. I'd go with option B.
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Anisha
4 months ago
I vaguely remember that we need to use the `spark.read` method to load the data, but I'm confused about how to specify the path correctly.
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Belen
4 months ago
I feel like the answer might involve reading the Delta table into a DataFrame and then chaining the drop method. I hope I remember the right order!
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Serina
5 months ago
I think we practiced a similar question where we had to manipulate a DataFrame. It involved using `withColumn` and `drop`, but I can't recall the exact syntax.
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Yong
5 months ago
I remember something about using the `drop` function in PySpark to remove a column, but I'm not entirely sure how to load the data first.
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Chaya
5 months ago
Easy peasy! I'll just read in the data, confirm the column names, and then use that drop() method to get rid of the star_rating column. Shouldn't take more than a few lines of code.
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Curt
5 months ago
Ugh, I'm a little unsure about the location path part. I'll need to double-check how to properly load the data from that specified location. But once I have the DataFrame, dropping the column should be easy.
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Susy
5 months ago
No problem, I've done something like this before. I'll load the data, check the column names, and then use DataFrame.drop() to remove the star_rating column.
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Lasandra
6 months ago
Okay, let's see. I'll need to figure out how to load the data from the specified location path first. Then I can drop the column using DataFrame.drop().
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Jani
6 months ago
Hmm, this seems pretty straightforward. I'd start by loading the data into a DataFrame, then use the drop() method to remove the star_rating column.
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