Databricks Exam Databricks Machine Learning Professional Topic 6 Question 32 Discussion

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

Question #: 32
Topic #: 6

[All Databricks Machine Learning Professional Questions]

A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.

Which of the following lines of code can they use to accomplish this task?

Amlflow.sklearn.autolog()

Bmlflow.spark.autolog()

Cspark.conf.set(''autologging'', True)

DIt is not possible to automatically log MLflow runs.

Emlflow.autolog()

Show Suggested Answer

Suggested Answer: D

by Juliann at Dec 08, 2024, 12:30 PM

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Ricarda

2 days ago

Definitely agree with E, it covers all libraries.

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Fabiola

8 days ago

I think option E is the right choice!

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Malcom

13 days ago

I have a feeling that option E, `mlflow.autolog()`, is the best choice since it seems to be the most comprehensive for enabling autologging across different libraries.

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Staci

19 days ago

I'm a bit confused about the workspace configurations. Does that affect how autologging works? I feel like `spark.conf.set('autologging', True)` might not be the right approach.

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Patti

24 days ago

I think we practiced a question similar to this, and I recall that `mlflow.sklearn.autolog()` is specific to scikit-learn, so it might not be the right choice here.

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Barabara

1 month ago

I remember we discussed how `mlflow.autolog()` is a general function that works across different libraries, but I'm not entirely sure if it covers all versions of Databricks Runtime.

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Major

1 month ago

This is a great question. I'm feeling pretty confident about this one. The answer is definitely E, mlflow.autolog(). That's the universal way to enable MLflow Autologging across the board.

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Rosio

1 month ago

Ah, I see now. The key is that mlflow.autolog() is the general function that will handle all the different machine learning libraries and Databricks settings. That's a great tip, I'll make sure to remember that for the exam.

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Dierdre

1 month ago

I'm a bit confused here. Is it really that simple? I was thinking we might need to do something more specific to handle the different library versions and Databricks configurations. Let me double-check the options.

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Sharan

1 month ago

Okay, I think I've got it. The answer is E, mlflow.autolog(). That should work regardless of the Databricks Runtime version or workspace-wide configurations.

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Lynna

1 month ago

Hmm, this seems like a tricky one. I'll need to carefully read through the options and think about which one would enable MLflow Autologging across all machine learning libraries and Databricks Runtime versions.

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Janine

6 months ago

Option E all the way! It's like the Swiss Army knife of MLflow Autologging - one command to rule them all.

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Bettina

6 months ago

I'd go with option E. It's the most generic and versatile solution, and the question specifically asks for a way to enable MLflow Autologging 'no matter what' version or configuration is used.

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Mona

4 months ago

User4: Let's go with E then, it's the safest bet.

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Micaela

4 months ago

User 3: Yeah, option E covers all versions and configurations.

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Farrah

4 months ago

User 2: I agree, it seems like the most versatile solution.

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Donette

5 months ago

User 1: I think option E is the best choice.

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Cathrine

5 months ago

User3: Yeah, E would cover all scenarios mentioned in the question.

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Kayleigh

5 months ago

User2: I agree, it seems like the most versatile option.

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Arminda

5 months ago

User1: I think option E is the best choice.

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Hortencia

6 months ago

Option D? Really? That's just defeatist. Of course it's possible to automatically log MLflow runs, otherwise the question wouldn't even make sense.

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Matthew

5 months ago

E) mlflow.autolog()

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Tesha

5 months ago

B) mlflow.spark.autolog()

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Louis

5 months ago

Option D is definitely defeatist. We can definitely automatically log MLflow runs.

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Theodora

5 months ago

E) mlflow.autolog()

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Tequila

5 months ago

B) mlflow.spark.autolog()

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Ailene

5 months ago

A) mlflow.sklearn.autolog()

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Lavonna

5 months ago

A) mlflow.sklearn.autolog()

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Tamar

6 months ago

Haha, option C is a bit of a stretch. Trying to set a 'autologging' configuration in Spark seems like an overly complicated way to achieve this task.

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Merilyn

6 months ago

I'm not sure about that. Option B seems more specific to Spark, and the question mentions using multiple machine learning libraries, not just Spark.

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Rene

6 months ago

Hmm, I think option E is the correct answer. It seems like the most straightforward way to enable MLflow Autologging across all machine learning libraries in the notebook.

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