Databricks Exam Databricks Machine Learning Professional Topic 12 Question 21 Discussion

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

Question #: 21
Topic #: 12

[All Databricks Machine Learning Professional Questions]

A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.

The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

AReplace update_registered_model with update_model_version

BThere no changes necessary

CReplace description with artifact

CReplace client.update_registered_model with mlflow

DAdd a Python model as an argument to update_registered_model

Show Suggested Answer

Suggested Answer: B

by Lorean at Jul 18, 2024, 11:29 AM

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Luther

2 days ago

I think B is correct, no changes needed.

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Hillary

8 days ago

Looks like option A is the right move!

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Thersa

14 days ago

Adding a Python model as an argument sounds familiar, but I’m not confident if that’s the right approach for this specific task.

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Roy

19 days ago

I feel like we should be using `mlflow` directly instead of `client.update_registered_model`, but I need to double-check that.

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Alise

24 days ago

I remember practicing a similar question where we had to update model descriptions, but I can't recall if any changes were actually needed.

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Mike

1 month ago

I think we might need to replace `update_registered_model` with something else, but I'm not sure which option is correct.

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Delmy

1 month ago

The question seems straightforward enough. The key is to identify the correct function to use for updating the model description. Since the question mentions the "model_description" variable, I think option A, replacing "update_registered_model" with "update_model_version", is the way to go.

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Raina

1 month ago

I'm not too familiar with the MLflow Model Registry, so this question is a bit tricky for me. I'll need to review the documentation and make sure I understand the different functions and how they're used before I can confidently answer this.

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Avery

1 month ago

Okay, I think I've got it. Based on the question, we need to replace "update_registered_model" with "update_model_version" to update the text description for the specific model project. That should do the trick!

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Domingo

1 month ago

Hmm, I'm a bit confused by the code snippet. It looks like we're using the MLflow client to interact with the Model Registry, but I'm not sure if the "update_registered_model" function is the right one to use here. I'll need to double-check the MLflow documentation to be sure.

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Lizbeth

1 month ago

I think the key here is to focus on the specific function call that needs to be updated. The question mentions updating the "model_description" variable, so I would guess that the "update_registered_model" function is the one we need to modify.

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Dexter

1 month ago

Okay, let me think this through. Billing Data and Tags seem like the most relevant options here for visibility into resource management and billing. I'll go with those two.

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Barrie

1 month ago

This question seems pretty straightforward. I think I can tackle it by carefully analyzing the requirements and the different solution options presented.

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Malika

1 month ago

Okay, I've got a strategy. I'll read through the options and think about which one best describes the purpose of cleaning and normalizing data.

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Crissy

1 year ago

I'm just wondering if the 'model' project in the MLflow Model Registry is self-aware enough to appreciate its own text description. Food for thought.

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Barb

1 year ago

Adding a Python model as an argument? Sounds like they're trying to turn this into a full-blown software engineering project. Let's keep it simple, folks.

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