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]

Workflow design patterns for the machine learning pipelines:

AAim to explain how the machine learning model works.

BRepresent a pipeline with directed acyclic graph (DAG).

CSeek to simplify the management of machine learning features.

DSeparate inputs from features.

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

Limited Time Offer

25%

Off

Get Premium AIP-210 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Richelle

6 months ago

Haha, I bet the exam writers just threw in A to see who would bite. That's like asking 'Is the sky blue?' for an ML question.

upvoted 0 times

...

Theodora

6 months ago

B and C are both good options. Workflow patterns help with both visualizing the pipeline and organizing the feature engineering process.

upvoted 0 times

Laurene

6 months ago

I agree, having a clear visualization of the pipeline and managing features efficiently is crucial for machine learning projects.

upvoted 0 times

...

Ruthann

6 months ago

C) Seek to simplify the management of machine learning features.

upvoted 0 times

...

Mabel

6 months ago

B) Represent a pipeline with directed acyclic graph (DAG).

upvoted 0 times

...

Steffanie

7 months ago

D seems like the right answer to me. Separating inputs from features is fundamental for maintaining clean and scalable ML pipelines.

upvoted 0 times

...

Jennifer

7 months ago

I'd go with C. The goal is to simplify the management of features, which is a crucial aspect of building effective ML models.

upvoted 0 times

Pok

6 months ago

That's a good point. Simplifying feature management can definitely improve the efficiency of machine learning pipelines.

upvoted 0 times

...

Pete

6 months ago

C) Seek to simplify the management of machine learning features.

upvoted 0 times

...

Dorinda

6 months ago

That's a good point. Simplifying feature management can definitely improve the efficiency of machine learning pipelines.

upvoted 0 times

...

Bobbye

6 months ago

Definitely, simplifying feature management can lead to more effective machine learning pipelines.

upvoted 0 times

...

Keneth

6 months ago

C) Seek to simplify the management of machine learning features.

upvoted 0 times

...

Leota

6 months ago

That's a good choice. Managing features efficiently is key for successful ML models.

upvoted 0 times

...

Rodolfo

6 months ago

C) Seek to simplify the management of machine learning features.

upvoted 0 times

...

Maurine

7 months ago

Definitely B. Workflow design patterns for machine learning pipelines are all about representing the data flow as a directed acyclic graph. That's the whole point!

upvoted 0 times