CertNexus Exam AIP-210 Topic 3 Question 19 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 19
Topic #: 3

[All AIP-210 Questions]

Which of the following are true about the transform-design pattern for a machine learning pipeline? (Select three.)

It aims to separate inputs from features.

AIt encapsulates the processing steps of ML pipelines.

BIt ensures reproducibility.

CIt represents steps in the pipeline with a directed acyclic graph (DAG).

DIt seeks to isolate individual steps of ML pipelines.

EIt transforms the output data after production.

Show Suggested Answer

Suggested Answer: B

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.

by Yvonne at May 19, 2024, 02:33 PM

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Louvenia

11 days ago

I hear the transform-design pattern is the next big thing in ML - kind of like the Hokey Pokey, but with more data wrangling and less foot-shaking.

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Telma

12 days ago

Wait, so it doesn't transform the output data *during* production? What is this, amateur hour? Just kidding, this pattern sounds pretty legit.

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Yuette

14 days ago

Transforming the output data after production? Interesting, I wonder if that's for post-processing or model updates. Either way, good to have that flexibility built-in.

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Alana

17 days ago

Representing the pipeline steps as a directed acyclic graph is clever. Helps visualize the flow and dependencies. Now if only I could get my code to look that organized...

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An

19 days ago

Ah, I see it also ensures reproducibility. That's crucial for maintaining quality and trust in my models. Definitely going to look into this pattern more.

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Lenora

12 hours ago

A) It encapsulates the processing steps of ML pipelines.

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Dalene

1 months ago

The transform-design pattern seems like a great way to organize my ML pipelines. I like how it separates inputs from features and isolates the individual steps - that should make debugging a lot easier.

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