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Databricks Exam Databricks Machine Learning Professional Topic 5 Question 33 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 33
Topic #: 5
[All Databricks Machine Learning Professional Questions]

Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

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Suggested Answer: C

by Darrel at Jan 11, 2025, 09:51 PM

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Serina
2 days ago
Definitely A! It’s the only one that makes sense.
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Elina
8 days ago
I think it's A, right?
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Kent
13 days ago
I’m leaning towards `mlflow.shap.log_explanation`, but I might be mixing it up with another function.
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Ocie
19 days ago
I vaguely recall a practice question where `mlflow.log_figure` was mentioned, but I don't think it was specifically for Shapley plots.
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Leonie
24 days ago
I feel like none of these options really fit what I practiced. Maybe "None of these operations can accomplish the task" is correct?
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Leonida
1 month ago
I think I remember something about `mlflow.shap.log_explanation` being related to Shapley values, but I'm not entirely sure if it's the right choice.
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Joseph
1 month ago
Ah, I see. I was leaning towards D or E, but now I'm reconsidering. I'll make sure to review the MLflow documentation before answering this one.
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Matthew
1 month ago
I think B might be the right answer here. From what I understand, none of the MLflow operations can automatically calculate and log a Shapley feature importance plot. We'd need to do that manually.
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Julian
1 month ago
Okay, let me see. I know MLflow has some built-in functionality for logging model explanations, so I'm guessing A or C could be the right answer. I'll have to double-check the documentation to be sure.
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Brianne
1 month ago
Hmm, I'm not too familiar with the MLflow operations, so I'm a bit unsure about this one. I'll have to think it through carefully.
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Brinda
1 month ago
I'm pretty sure the answer is A, mlflow.shap.log_explanation. That seems like the most direct way to log a Shapley feature importance plot.
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Mireya
6 months ago
E) client.log_artifact? Is that like logging your pet rock as a feature importance plot? Good luck explaining that one to the graders!
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Noemi
6 months ago
D) mlflow.log_figure? Really? That's like trying to log a Picasso with a selfie stick. Not gonna cut it, my friend.
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Alona
5 months ago
A) mlflow.shap.log_explanation is the way to go for calculating and logging a Shapley feature importance plot.
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Sabra
5 months ago
E) client.log_artifact doesn't seem like the right operation for this specific task.
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Jules
5 months ago
C) mlflow.shap might also be a good option to consider.
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Thurman
5 months ago
A) mlflow.shap.log_explanation sounds like the right choice for this task.
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Alita
6 months ago
C) mlflow.shap sounds like the winner to me. I mean, it's right there in the name, isn't it? MLflow + Shapley = magic.
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Jerry
5 months ago
E) client.log_artifact
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Wai
5 months ago
C) mlflow.shap sounds like the winner to me. I mean, it's right there in the name, isn't it? MLflow + Shapley = magic.
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Rosendo
5 months ago
A) mlflow.shap.log_explanation
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Helga
6 months ago
B) None of these operations can accomplish the task? Nah, that can't be right. I'm pretty sure there's an MLflow function specifically for logging Shapley plots.
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Bea
5 months ago
E) client.log_artifact
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Rima
5 months ago
C) mlflow.shap
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Micaela
6 months ago
A) mlflow.shap.log_explanation
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Jina
6 months ago
A) mlflow.shap.log_explanation sounds like the right answer here. I'm pretty sure that's the MLflow operation to log Shapley feature importance plots.
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Terry
7 months ago
I'm not sure, but I think D) mlflow.log_figure could also be a potential option for automatically logging the Shapley feature importance plot.
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Eric
7 months ago
I agree with Terrilyn, because mlflow.shap.log_explanation is specifically designed for calculating and logging Shapley feature importance plots.
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Terrilyn
7 months ago
I think the answer is A) mlflow.shap.log_explanation.
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