Databricks Exam Databricks Machine Learning Professional Topic 1 Question 20 Discussion

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

Question #: 20
Topic #: 1

[All Databricks Machine Learning Professional Questions]

A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.

Which of the following tools can be used to provide this type of continuous processing?

ASpark UDFs

B[Structured Streaming

CMLflow
D Delta Lake

EAutoML

Show Suggested Answer

Suggested Answer: C

by Magda at Jul 07, 2024, 12:06 PM

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Ling

1 months ago

Hey, I heard MLflow is the new hotness for machine learning ops. Maybe they can just throw some emojis at the data and it'll magically get processed.

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Marge

1 days ago

C: Spark UDFs might also be useful for data processing.

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Bernardine

4 days ago

B: Yeah, MLflow is great for managing the machine learning lifecycle.

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Gertude

13 days ago

A: I think MLflow could definitely help with that.

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Mari

2 months ago

Spark UDFs? That's just for extending Spark's functionality, not for continuous data processing. I'm with the others - Structured Streaming is the way to go.

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Marci

2 months ago

AutoML? Really? That's for automating the machine learning model development process, not data preprocessing. I'd say Structured Streaming is the clear winner here.

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Julie

1 days ago

Spark UDFs might work well for custom data processing functions within the pipeline.

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Helaine

7 days ago

I think MLflow could also be useful for tracking and managing the machine learning pipeline.

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Tijuana

16 days ago

I agree, Structured Streaming is the best choice for continuous processing.

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Sabina

25 days ago

Delta Lake is great for reliable data lakes, but maybe not the best fit for this specific task.

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Emerson

29 days ago

MLflow could also be useful for tracking experiments and managing the machine learning lifecycle.

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Kerrie

2 months ago

I agree, Structured Streaming is the best choice for continuous processing.

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Janey

2 months ago

Hmm, I'm not sure about Structured Streaming. Isn't that more for stream processing? I feel like Delta Lake might be a better fit since it can handle batch processing as well.

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Dortha

22 days ago

Let's go with Delta Lake for the continuous processing pipeline.

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Annmarie

28 days ago

I'm not sure about Structured Streaming either, but Delta Lake seems like a versatile option.

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Lucina

1 months ago

I agree, Delta Lake can handle both batch and stream processing.

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Susana

2 months ago

I think Delta Lake would be a good choice for continuous processing.

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Loren

3 months ago

I think Structured Streaming is the right choice here. It's specifically designed for continuous, real-time data processing, which is exactly what the team needs for their machine learning pipeline.

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