Databricks Exam Databricks-Machine-Learning-Professional Topic 8 Question 19 Discussion

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

Question #: 19
Topic #: 8

[All Databricks-Machine-Learning-Professional Questions]

Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

Apython_function provides no benefits over the built-in library-specific model flavors

Bpython_function can be used to deploy models in a parallelizable fashion

Cpython_function can be used to deploy models without worrying about which library was used to create the model

Dpython_function can be used to store models in an MLmodel file

Epython_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments

Show Suggested Answer

Suggested Answer: B

by Alva at Jul 03, 2024, 08:45 PM

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Angelica

5 months ago

Hmm, I'm torn between C and E. Maybe I should just roll a die to decide. Or, you know, use my brain and pick C.

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Tiara

5 months ago

A? Really? That's like saying a Ferrari has no benefits over a tricycle. C is the clear winner here.

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Latosha

3 months ago

C is definitely the better choice. It makes deployment much simpler.

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Sophia

3 months ago

C) python_function can be used to deploy models without worrying about which library was used to create the model

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Elenore

3 months ago

A) python_function provides no benefits over the built-in library-specific model flavors

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Dahlia

3 months ago

I agree, C is definitely the better choice

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Shayne

4 months ago

C) python_function can be used to deploy models without worrying about which library was used to create the model

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Cary

4 months ago

A) python_function provides no benefits over the built-in library-specific model flavors

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Allene

5 months ago

Yes, that's true. It can make deployment much faster and efficient.

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Maia

5 months ago

I believe option B is also important, being able to deploy models in a parallelizable fashion.

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Jose

5 months ago

Wow, the python_function model flavor sounds like a real Swiss Army knife! I'll go with E - the flexibility to deploy in any environment is super valuable.

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Albina

5 months ago

I'm going with B. The ability to deploy models in a parallelizable fashion is crucial for large-scale production deployments.

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Alecia

4 months ago

E is important as well, deploying models without worrying about the environment.

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Lavonna

5 months ago

D sounds useful too, being able to store models in an MLmodel file.

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Mireya

5 months ago

I think C is also a great advantage, not having to worry about the library used for the model.

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Vallie

5 months ago

I agree, deploying models in a parallelizable fashion is key for scalability.

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Nana

5 months ago

Option C makes the most sense to me. Being able to deploy models without worrying about the underlying library seems like a big advantage.

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Elbert

5 months ago

Definitely, not having to worry about the underlying library is a huge benefit.

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Virgina

5 months ago

I agree, option C does sound like a big advantage.

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Lenora

6 months ago

I agree with Allene, not worrying about which library was used is a big benefit.

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Allene

6 months ago

I think option C is the advantage of using python_function(pyfunc) model flavor.

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