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APMG-International Artificial-Intelligence-Foundation Exam - Topic 1 Question 16 Discussion

Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?
A) Takes longer to deliver all functional requirements.
B) Discourages collaboration and cross boundary communication.
C) Takes longer to complete the design phase of the project.
D) Discourages revisiting and revising any prior phase once it is complete.

APMG-International Artificial-Intelligence-Foundation Exam - Topic 1 Question 16 Discussion

Actual exam question for APMG-International's Artificial-Intelligence-Foundation exam
Question #: 16
Topic #: 1
[All Artificial-Intelligence-Foundation Questions]

Which factor of a Waterfall' approach is most likely to result in the failed delivery of an Al project?

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Suggested Answer: A

Al is designed to take on routine and monotonous tasks, freeing up humans to take on more complex, higher value work. This can include tasks such as research, problem-solving, and decision-making. This shift in work roles is expected to increase productivity and efficiency, allowing humans to focus on more creative and innovative tasks. For example, robots can be used to automate mundane manufacturing processes, freeing up human workers to take on jobs that require more creative thinking and problem-solving.


by Leontine at Nov 17, 2024, 05:23 PM

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Lilli
9 months ago
B is the worst, communication is key in any project!
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Adell
9 months ago
I disagree, C isn't as critical as the others.
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Johana
10 months ago
Surprised that people overlook A, it really drags things out!
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Tawna
10 months ago
I think D is a big issue too, can't just lock in decisions.
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Dylan
10 months ago
Definitely B, lack of collaboration kills projects.
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Beata
10 months ago
I recall that the design phase is crucial, but I wonder if C is the main issue. It seems like all phases are important, but maybe D is more critical in the long run.
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Rolland
10 months ago
I practiced a question similar to this, and I think A is a strong contender because delays in delivering requirements can really impact the timeline.
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Cecily
11 months ago
I'm not entirely sure, but I feel like B could be a big problem too. Lack of collaboration can really hinder an AI project.
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Johnna
11 months ago
I remember discussing how the Waterfall model can lead to issues if there's no room for revisiting earlier phases. So, I think D might be the right answer.
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Melda
11 months ago
I've got a good feeling about this one. The Waterfall method discourages revisiting and revising prior phases, which could be a major issue for an AI project where requirements and design may need to be adjusted as the project progresses. I'm going with option D.
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Micah
11 months ago
I'm a bit confused by this question. The Waterfall approach has its downsides, but I'm not sure which one would be the most likely to cause an AI project to fail. I'll have to think about this a bit more.
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Yuki
11 months ago
Okay, let me think this through. The Waterfall method doesn't really encourage collaboration, which could be a problem for an AI project that requires a lot of cross-functional work. I'm leaning towards option B.
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Jina
11 months ago
Hmm, this is a tricky one. I think the key is to focus on the factors that are most likely to cause issues with AI projects specifically. The Waterfall approach can be rigid, so I'm guessing the answer has to do with that.
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Mirta
1 year ago
This is a no-brainer. The waterfall model is about as far away from an agile, iterative AI approach as you can get. D is the answer, hands down. Now, who wants to play a game of 'Guess the Hyperparameter'?
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Chau
1 year ago
D) Discourages revisiting and revising any prior phase once it is complete.
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Mohammad
1 year ago
C) Takes longer to complete the design phase of the project.
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Laura
1 year ago
B) Discourages collaboration and cross boundary communication.
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Aileen
1 year ago
A) Takes longer to deliver all functional requirements.
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Ettie
1 year ago
A) Takes longer to deliver all functional requirements? Pfft, with an AI project, you're never really 'done' anyway. D is the clear winner here. Waterfall and AI just don't mix.
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Pamella
1 year ago
Hmm, I was leaning towards B, but D makes a lot of sense. The lack of collaboration and revisions is a killer for an AI project. Gotta keep that flexibility, you know?
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Edgar
1 year ago
User 3: I think D is the biggest risk factor for failed delivery.
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Moon
1 year ago
User 2: Definitely, sticking to a rigid plan can really hinder progress.
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Melynda
1 year ago
User 1: I agree, flexibility is key for AI projects.
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Leah
1 year ago
The waterfall model is way too rigid for an AI project. D is definitely the right answer here. You can't just lock in the design and expect it to work for a complex AI system.
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Barb
1 year ago
That's why agile methodologies are better suited for AI projects.
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Estrella
1 year ago
Yeah, once you lock in the design, it's hard to make changes for AI projects.
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Avery
1 year ago
I agree, D is a big problem with the waterfall approach.
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Tonette
1 year ago
I think the correct answer is D. The waterfall approach discourages revisiting and revising any prior phase once it is complete, which can be detrimental for an AI project that requires an iterative and flexible approach.
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Lizette
1 year ago
C is crucial too, as the design phase is critical for the success of the project.
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Nieves
1 year ago
True, A can also be a problem because functional requirements may change over time.
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Hillary
1 year ago
I think B is also important because collaboration is key in AI projects.
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Lavonne
1 year ago
I agree, D is a major factor that can lead to failed delivery of an AI project.
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Arlette
1 year ago
But what about A) Takes longer to deliver all functional requirements? If we take too long to deliver, the project might become outdated before it's even completed.
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Theresia
1 year ago
I agree with Alverta. If we can't go back and make changes as needed, it could lead to major issues down the line.
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Alverta
1 year ago
I think the factor that is most likely to result in the failed delivery of an AI project is D) Discourages revisiting and revising any prior phase once it is complete.
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