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Databricks Exam Databricks Machine Learning Professional Topic 6 Question 35 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 35
Topic #: 6
[All Databricks Machine Learning Professional Questions]

A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.

Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

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Suggested Answer: B

by Alease at Feb 06, 2025, 10:17 PM

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Kattie
2 days ago
I thought it was mlflow.log_metric?
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Claudia
8 days ago
It's definitely mlflow.log_param for logging single values!
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Alayna
13 days ago
I thought mlflow.log_metric was for tracking performance metrics, so I'm leaning towards D as well, but I could be wrong.
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Myong
19 days ago
I practiced a similar question where we had to log hyperparameters, and I think that was also about using mlflow.log_param.
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Glendora
24 days ago
I'm not entirely sure, but I feel like logging metrics is more about performance, so maybe it's not C.
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Margarett
1 month ago
I remember we discussed logging parameters in MLflow, so I think it might be option D, mlflow.log_param.
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Tamekia
1 month ago
I feel pretty confident about this one. The question is specifically asking about logging a single value, and mlflow.log_param is the function designed for that purpose. I think the answer is clear.
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Karma
1 month ago
Okay, let me see if I can break this down. We're looking for a way to log a single value, like the number of trees. I'm pretty sure mlflow.log_metric is for logging metrics, which are usually more complex than just a single value. So I'm leaning towards D, mlflow.log_param.
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Nida
1 month ago
Hmm, I'm a bit unsure about this one. I know mlflow has different logging functions, but I'm not totally clear on the differences between them. I'll have to think this through carefully.
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Gwenn
1 month ago
This seems pretty straightforward. I think the answer is D, mlflow.log_param, since that's designed to log single values like the number of trees in a random forest.
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Catarina
6 months ago
Woah, this is tricky. Maybe I should just log the whole random forest model and call it a day? Nah, I'll go with D) mlflow.log_param.
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Cecily
6 months ago
Wait, are we sure we can't just carve the number of trees into the forest and call it a day? Kidding, I'll go with D) mlflow.log_param.
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Maryln
5 months ago
User 3: Yeah, logging the parameter values is important for tracking the model runs.
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Pearline
5 months ago
User 2: I think D) mlflow.log_param is the right choice here.
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Lon
5 months ago
User 1: Haha, that would be a unique approach!
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Elenora
7 months ago
I'm feeling confident about this one. The number of trees is a single value, so C) mlflow.log_metric seems like the right choice.
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Regenia
6 months ago
Yes, that's correct. It's specifically designed for logging metrics in MLflow.
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Linn
6 months ago
I agree, C) mlflow.log_metric is the way to go for logging single values like the number of trees.
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Edward
7 months ago
I'm not sure, but I think D) mlflow.log_param could also be a valid option to store single values in MLflow.
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Jessenia
7 months ago
Hmm, this seems straightforward. The number of trees in a random forest is a parameter, so I'll go with D) mlflow.log_param.
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Regenia
7 months ago
I agree with Ilona, because logging metrics is the way to track single values like the number of trees in a random forest.
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Ilona
7 months ago
I think the answer is C) mlflow.log_metric.
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