Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

CertNexus Exam AIP-210 Topic 5 Question 22 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 22
Topic #: 5
[All AIP-210 Questions]

Workflow design patterns for the machine learning pipelines:

Show Suggested Answer Hide 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 Lorrie at Jul 05, 2024, 02:42 AM

Limited Time Offer

25%

Off

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

Contribute your Thoughts:

Submit Cancel
Leonor
7 months ago
Separating inputs from features can improve the model's performance.
upvoted 0 times
...
Cheryl
7 months ago
I believe representing a pipeline with a DAG is crucial for understanding the flow.
upvoted 0 times
...
Odette
7 months ago
I'm going with B. It's the workflow equivalent of 'everything is a graph' in the ML world.
upvoted 0 times
Daniel
7 months ago
That's a good choice! DAGs are really helpful for visualizing and understanding the flow of machine learning pipelines.
upvoted 0 times
...
Genevive
7 months ago
B) Represent a pipeline with directed acyclic graph (DAG).
upvoted 0 times
...
...
Stephaine
7 months ago
I agree, they help in organizing the process efficiently.
upvoted 0 times
...
Lezlie
8 months ago
D is a bit too specific. B captures the essence of workflow design patterns for ML pipelines.
upvoted 0 times
Jerry
7 months ago
C
upvoted 0 times
...
Virgie
7 months ago
B
upvoted 0 times
...
Marvel
7 months ago
A
upvoted 0 times
...
...
Omega
8 months ago
C is interesting, but B is the best choice here. Gotta love those directed acyclic graphs!
upvoted 0 times
...
Lemuel
8 months ago
I agree with B. It's all about the data flow, not explaining the model itself.
upvoted 0 times
Devon
7 months ago
Yeah, I agree. Representing the pipeline with a DAG makes it easier to understand.
upvoted 0 times
...
Layla
7 months ago
I think B is the way to go. It's all about the data flow.
upvoted 0 times
...
...
Marjory
8 months ago
I think workflow design patterns are important for machine learning pipelines.
upvoted 0 times
...
Lashawnda
8 months ago
B is the correct answer. Workflow design patterns for ML pipelines use DAGs to represent the pipeline flow.
upvoted 0 times
Lashawn
8 months ago
That makes sense. DAGs are great for visualizing the flow of tasks in a machine learning pipeline.
upvoted 0 times
...
Cherilyn
8 months ago
B is the correct answer. Workflow design patterns for ML pipelines use DAGs to represent the pipeline flow.
upvoted 0 times
...
...

Save Cancel