Databricks Machine Learning Professional Exam - Topic 3 Question 37 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam

Question #: 37
Topic #: 3

[All Databricks Machine Learning Professional Questions]

A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.

Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

Amlflow.load_model(model_uri)

Bclient.list_artifacts(run_id)['feature-importances.csv']

Cmlflow.sklearn.load_model(model_uri)

DThis can only be viewed in the MLflow Experiments UI

Eclient.pyfunc.load_model(model_uri)

Show Suggested Answer

Suggested Answer: A

by Chantell at Mar 19, 2025, 04:30 PM

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Miriam

5 months ago

I’m not sure about D, I thought we could access it programmatically.

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Anika

5 months ago

Wait, can you really only view it in the UI? That seems odd!

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Sage

5 months ago

Definitely agree with C, it’s the proper way to load scikit-learn models.

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Luz

6 months ago

Option A is also a valid choice, but not for scikit-learn models.

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Ivan

6 months ago

I think option C is the right one!

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Alex

6 months ago

I thought we could only view feature importances in the UI, but I feel like there's a way to load the model directly too.

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Merlyn

6 months ago

I vaguely recall that `client.list_artifacts` is used for listing files, but I don't think it actually loads the model.

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Kerry

7 months ago

I think we practiced a similar question where we had to retrieve model artifacts. I feel like option C is the right one since it specifies sklearn.

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Mirta

7 months ago

I remember we discussed how to load models with MLflow, but I'm not sure if it's `mlflow.load_model` or `mlflow.sklearn.load_model`.

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Mozelle

7 months ago

Hmm, I'm not sure about this one. I'll have to review my notes on MLflow and model persistence to make the best choice.

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Selene

7 months ago

Ah, I think I've got it! The key is that the data scientist terminated their Spark cluster, so we need to use a method that doesn't rely on the Spark session.

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Farrah

7 months ago

Wait, I'm a bit confused. Isn't there something about Spark in the question? I wonder if that changes how we need to approach this.

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Lorean

8 months ago

Okay, let's see. I'm pretty sure I've used mlflow.load_model() before to restore a model, so that's my first guess.

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Lauran

8 months ago

Hmm, this looks like a tricky one. I'll need to think through the options carefully to make sure I don't miss anything.

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Tawna

1 year ago

Option C is the way to go. Hey, at least it's not asking us to restore the model from a backup tape or something!

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Lavera

1 year ago

Yeah, MLflow is a lifesaver when it comes to managing and restoring model objects.

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Arlene

1 year ago

Option C is definitely the right choice. MLflow makes it easy to load the model object.

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Patrick

1 year ago

I think D) This can only be viewed in the MLflow Experiments UI is incorrect because we need to access the feature_importances_ in the code

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Fairy

1 year ago

I'm going with C. Gotta love those scikit-learn-specific utilities, they make my life so much easier. *sarcasm intensifies*

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Ezekiel

1 year ago

I'm not sure, but I think A) mlflow.load_model(model_uri) might also work since it's a general model loading function in MLflow

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Bok

1 year ago

D is a joke, right? I'm not gonna look at feature importances in the MLflow UI, that's what the code is for!

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Hana

1 year ago

E) client.pyfunc.load_model(model_uri)

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Louvenia

1 year ago

C) mlflow.sklearn.load_model(model_uri)

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Quentin

1 year ago

A) mlflow.load_model(model_uri)

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Lea

1 year ago

I agree with Monroe, because mlflow.sklearn.load_model(model_uri) is specifically for loading scikit-learn models

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Glen

1 year ago

I'm not sure why you can't just use `mlflow.load_model()` to restore the model. Seems like a waste of time to have a separate function for scikit-learn models.

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Louis

1 year ago

I'm not sure why you can't just use `mlflow.load_model()` to restore the model. Seems like a waste of time to have a separate function for scikit-learn models.

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Dottie

1 year ago

C) mlflow.sklearn.load_model(model_uri)

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Wenona

1 year ago

A) mlflow.load_model(model_uri)

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Monroe

1 year ago

I think the correct answer is C) mlflow.sklearn.load_model(model_uri)

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Jutta

1 year ago

Option C looks like the correct answer. You can use `mlflow.sklearn.load_model()` to restore the original model object and access the `feature_importances_`.

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