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Databricks Machine Learning Professional Exam - Topic 2 Question 38 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 38
Topic #: 2
[All Databricks Machine Learning Professional Questions]

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.

They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.

Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

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Suggested Answer: E

by Fletcher at Apr 03, 2025, 09:45 PM

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Shawnta
2 months ago
Wait, can you really nest runs like that? Sounds tricky!
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Skye
2 months ago
I think adding nested=True to the parent run is also a good idea.
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Lenna
2 months ago
You need to indent the child run blocks!
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Mitsue
3 months ago
Removing nested=True from child runs might help too.
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Galen
3 months ago
Totally agree, indentation is key here.
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Mari
3 months ago
Providing the same name for all runs seems off to me; I don't think that would help with nesting.
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Roslyn
3 months ago
I feel like the nested=True argument is crucial for the parent run, but I'm not sure if it needs to be removed from the child runs too.
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Royce
4 months ago
I think I saw a practice question where we had to use a nested argument, but I can't recall if it was for the parent or child runs.
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Huey
4 months ago
I remember something about indentation being important in Python, so maybe indenting the child run blocks could help?
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Micaela
4 months ago
I'm a little unsure about this one. The options all seem plausible, but I'm not confident which one is the right change to make. I'll need to review the MLflow documentation to make sure I understand the nesting behavior correctly.
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Annamae
4 months ago
Adding the nested=True argument to the parent run and removing it from the child runs sounds like a good strategy. That should help establish the proper hierarchy between the parent and child runs in MLflow.
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Novella
4 months ago
Okay, let me see. I'm guessing we need to indent the child run blocks within the parent run block to get the nesting right. That seems like the most straightforward solution.
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Elina
5 months ago
Hmm, I'm a bit confused. The code block doesn't seem to be nesting the runs as expected, but I'm not sure which change would fix that. I'll need to think through the options carefully.
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Dorethea
5 months ago
I think the key here is to properly nest the child runs under the parent run. Based on the options, I'd say adding the nested=True argument to the parent run and removing it from the child runs should do the trick.
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Malcolm
10 months ago
Hmm, I'm not sure. Maybe we should just print('Hello, World!') and call it a day.
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Joanne
8 months ago
User3: E) Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
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Vicente
9 months ago
User2: B) Add the nested=True argument to the parent run
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Josphine
9 months ago
User1: A) Indent the child run blocks within the parent run block
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Becky
10 months ago
But wouldn't adding the nested=True argument to the parent run also achieve the same result?
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Luis
10 months ago
This is a classic case of not following the MLflow documentation. Option E is the correct answer, no doubt about it.
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Flo
10 months ago
That makes sense, it will help nest the child runs under the parent run in MLflow.
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Zachary
10 months ago
No, they should actually add the nested=True argument to the parent run and remove it from the child runs.
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Dan
10 months ago
I think the data scientist should add the nested=True argument to the parent run.
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Margot
10 months ago
I agree with Ammie. It makes sense to nest the child runs under the parent run.
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Katlyn
10 months ago
I think the key is to indent the child run blocks within the parent run block. That should do the trick. Option A is the way to go.
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Lorrie
9 months ago
Exactly, it's a small adjustment but it will make a big difference in organizing the runs.
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Tricia
9 months ago
Great, so the data scientist just needs to make that simple change in the code.
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Chanel
9 months ago
I agree, that's the key to nesting the child runs under the parent run in MLflow.
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Shawna
9 months ago
Option A is correct. Indenting the child run blocks within the parent run block will nest them properly.
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Ammie
10 months ago
I think the data scientist should indent the child run blocks within the parent run block.
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Rozella
11 months ago
The nested=True argument should be added to the parent run to nest the child runs under it. Option B looks good to me.
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Leatha
9 months ago
Good choice, let's update the code with nested=True for the parent run.
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Adelaide
9 months ago
Let's go with option B then.
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Dorcas
9 months ago
I agree, adding nested=True to the parent run should nest the child runs.
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Valentin
9 months ago
I think option B is the correct one.
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Talia
9 months ago
Great, let's make the change and see if it works.
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Shawna
9 months ago
That makes sense, let's go with option B then.
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Bong
9 months ago
I agree, adding nested=True to the parent run should nest the child runs.
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Arleen
10 months ago
I think option B is the correct one.
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