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Databricks Machine Learning Professional Exam - Topic 1 Question 25 Discussion

A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.Which of the following Databricks tools can be used to programmatically create the Job?
E) Databricks REST APIs
A) MLflow APIs
B) AutoML APIs
C) MLflow Client
D) Jobs cannot be created programmatically

Databricks Machine Learning Professional Exam - Topic 1 Question 25 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 25
Topic #: 1
[All Databricks Machine Learning Professional Questions]

A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.

Which of the following Databricks tools can be used to programmatically create the Job?

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Suggested Answer: E

by Sabina at Sep 19, 2024, 01:01 AM

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Emily
7 months ago
AutoML APIs are for model training, not job scheduling.
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Chana
8 months ago
No way, I thought jobs couldn't be created programmatically!
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Kristine
8 months ago
Wait, can’t you use MLflow Client too?
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Dino
8 months ago
Definitely agree with that!
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Kathryn
8 months ago
I think the Databricks REST APIs can do this!
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Kenny
9 months ago
I’m pretty certain that Jobs can be created programmatically, so option D seems wrong, but I’m stuck between the REST APIs and the MLflow Client.
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Tom
9 months ago
I feel like the MLflow Client could be relevant here, but I can't recall if it specifically handles job creation.
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Karan
9 months ago
I'm not entirely sure, but I remember something about MLflow APIs being more focused on tracking experiments rather than creating jobs.
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Micah
9 months ago
I think the Databricks REST APIs might be the right choice since they allow for programmatic interactions with various Databricks features.
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Hayley
9 months ago
I think the key here is that the question is asking about programmatic creation of a Databricks Job. That immediately makes me think of the Databricks REST APIs as the best solution. The other options like MLflow and AutoML don't seem as directly relevant for this use case.
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Jamika
9 months ago
Ah, this is a tricky one. I'm pretty confident the Databricks REST APIs are the right answer, but I'm not 100% sure. I'd want to review the documentation to be absolutely certain before answering.
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Lili
9 months ago
I think the Databricks REST APIs would be the way to go here. That's the most flexible and powerful option for programmatically creating and managing Databricks resources.
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Mitsue
9 months ago
Hmm, I'm not too sure about this one. I know we can use MLflow for a lot of Databricks functionality, but I'm not sure if it covers job creation. Maybe the MLflow Client could do it, but I'd have to double-check the docs.
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Emogene
9 months ago
Got it, the key is to make sure the guest user can access the network with multiple devices simultaneously. I'll go with option C.
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Sylvia
2 years ago
Hmm, this is a tough one. I'm torn between C and E, but I think E is the winner here. The Databricks REST APIs are just too powerful to ignore.
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Lavonda
2 years ago
Let's go with E) Databricks REST APIs then.
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Julio
2 years ago
I agree with you, E) Databricks REST APIs are very powerful.
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Alpha
2 years ago
I would go with A) MLflow APIs for creating the Job.
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Winfred
2 years ago
I think C) MLflow Client is the way to go.
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Gail
2 years ago
I'm going to go with E. The Databricks REST APIs are the way to go for programmatic job creation. I mean, what else would you use, a crystal ball?
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Melodie
2 years ago
This question is a piece of cake! E is the obvious answer. Who wouldn't use the Databricks REST APIs for this?
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Peter
2 years ago
D can't be right, because the question specifically states that the job needs to be created programmatically. That rules out D.
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Denise
2 years ago
I'm pretty sure the answer is C. The MLflow Client provides a Python API for managing MLflow entities, including jobs.
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Kent
2 years ago
Oh, thanks for clarifying!
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Cassandra
2 years ago
Actually, the correct answer is C) MLflow Client
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Blossom
2 years ago
No, I believe it's E) Databricks REST APIs
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Thea
2 years ago
I think it's A) MLflow APIs
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Caren
2 years ago
I think the answer is E. The Databricks REST APIs allow for programmatic creation of jobs, which is what the question is asking for.
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Nenita
2 years ago
I heard the Databricks engineers use their own REST APIs to create jobs while sipping on fancy lattes. #DeveloperLifestyle
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Sherly
2 years ago
That's interesting! I didn't know they used REST APIs for that. #DeveloperLifestyle
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Lynelle
2 years ago
Yes, you are correct. Databricks engineers use their REST APIs to create jobs programmatically.
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Narcisa
2 years ago
I think the answer is E) Databricks REST APIs.
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Louvenia
2 years ago
E all the way! Databricks REST APIs are the way to go. Anything else would be like trying to hammer a nail with a feather.
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Laurel
2 years ago
Using MLflow APIs might also be a good option depending on the specific requirements of the machine learning pipeline.
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Louann
2 years ago
I agree, Databricks REST APIs are definitely the best choice for programmatically creating a Job.
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Jordan
2 years ago
E all the way! Databricks REST APIs are the way to go. Anything else would be like trying to hammer a nail with a feather.
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Fannie
2 years ago
I'm not sure, but I think E) Databricks REST APIs could also be used for this task.
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Mignon
2 years ago
I agree with Jamal, using MLflow APIs makes sense for creating the Job programmatically.
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Laticia
2 years ago
D? Really? Jobs can definitely be created programmatically. That's just silly. I'm going with E.
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Yuonne
2 years ago
I agree, E) Databricks REST APIs seem like the right choice for this task.
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Jacquelyne
2 years ago
D? Really? Jobs can definitely be created programmatically. That's just silly. I'm going with E.
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Audry
2 years ago
I disagree, I believe C) MLflow Client is the tool to use for this.
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Cecil
2 years ago
I think A) MLflow APIs can be used to programmatically create the Job.
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Tamekia
2 years ago
Hmm, I'm not too sure about this one. Maybe C? MLflow Client could be a good choice, but I'm not 100% confident.
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Tresa
2 years ago
I think the answer is E. Databricks REST APIs. That's the most versatile option for programmatically creating jobs.
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Carri
2 years ago
I think MLflow Client might be useful too, but Databricks REST APIs seem to be the most popular choice.
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Mickie
2 years ago
MLflow APIs could also be a good option to consider for programmatically creating the job.
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Rex
2 years ago
I agree, using Databricks REST APIs would be the best choice for creating the job based on automated tests.
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Carylon
2 years ago
I think the answer is E. Databricks REST APIs. That's the most versatile option for programmatically creating jobs.
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Jamal
2 years ago
I think the answer is A) MLflow APIs.
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