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CompTIA DY0-001 Exam - Topic 2 Question 11 Discussion

Given these business requirements:Which of the following is the most likely optimization technique a data scientist would apply?
A) Constrained
B) Unconstrained
C) Non-iterative
D) Iterative

CompTIA DY0-001 Exam - Topic 2 Question 11 Discussion

Actual exam question for CompTIA's DY0-001 exam
Question #: 11
Topic #: 2
[All DY0-001 Questions]

Given these business requirements:

Which of the following is the most likely optimization technique a data scientist would apply?

Show Suggested Answer Hide Answer
Suggested Answer: A

You must optimize boat trips subject to strict resource limits (fuel, boat capacity, travel distance), making this a constrained optimization problem (e.g., solvable via linear programming).


by Adela at Nov 25, 2025, 11:11 AM

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Xenia
3 months ago
I feel like D) Iterative balances flexibility and control.
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Vanda
3 months ago
True, but without constraints, it might lead to overfitting.
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Catina
3 months ago
But B) Unconstrained could work too, right? More freedom!
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Marti
4 months ago
I’m leaning towards A) Constrained. It fits specific limits.
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Stephaine
4 months ago
Agreed! Iterative methods refine results over time.
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Lashawnda
4 months ago
I think D) Iterative is the best choice. It allows for adjustments.
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Ashley
5 months ago
Definitely D) Iterative, it's the most common approach!
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Rusty
5 months ago
Wait, why would anyone choose C) Non-iterative? That seems odd.
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Sommer
5 months ago
A) Constrained makes more sense for specific limits.
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Kris
5 months ago
I think D) Iterative is the way to go.
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Marlon
5 months ago
I'm with Theola on this one. Non-iterative is the way to go. Who has time for all that pesky iteration nonsense?
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Erick
5 months ago
Haha, constrained optimization? What is this, a prison for my data models? I'll take the D for Dynamite optimization, thank you very much.
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Elena
6 months ago
B. Unconstrained optimization is the obvious choice here. Gotta let those algorithms run wild!
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Reena
6 months ago
I lean towards iterative optimization since it seems to be the go-to for many data science problems, especially when dealing with complex datasets.
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Zita
6 months ago
I feel like we practiced a question similar to this, and I think iterative methods were emphasized for their flexibility in finding solutions.
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Moon
6 months ago
Ugh, optimization questions can be so tricky. I'm going to read through the requirements a few times and try to visualize how each technique might be applied. Hopefully that will help me narrow it down.
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Sunshine
6 months ago
This question is testing our understanding of optimization techniques. I think I have a good handle on this, so I'll carefully consider each option and pick the one that best fits the business requirements.
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Dominque
6 months ago
I'm a bit confused by the "non-iterative" and "iterative" options. I'll have to make sure I understand the difference between those approaches before answering.
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Theola
7 months ago
Definitely C. Non-iterative optimization is the way to go for these business requirements. Faster and more efficient.
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Renay
7 months ago
I think the answer is D. Iterative optimization is commonly used by data scientists to refine their models.
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Mable
7 months ago
I think I remember that constrained optimization is often used when there are specific limits or requirements, but I'm not entirely sure if that's the best choice here.
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Broderick
7 months ago
I'm a bit confused about the difference between constrained and unconstrained. I remember they both have their uses, but I can't recall which one fits this scenario better.
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Kathryn
7 months ago
I disagree, B) Unconstrained could be useful too.
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Mertie
8 months ago
Okay, let's see. I'm pretty sure constrained and unconstrained optimization are key concepts here. I'll need to review those to decide which is most likely.
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Nell
8 months ago
Hmm, this seems like a tricky one. I'll need to think carefully about the business requirements and how different optimization techniques might apply.
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Laurel
2 months ago
It’s all about the context of the problem!
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Erasmo
2 months ago
Constrained could also be relevant, though.
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Dong
3 months ago
I agree, iterative allows for adjustments.
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Alita
3 months ago
True, but iterative seems common in data science.
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Rodrigo
3 months ago
I think it depends on the specific requirements.
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