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NACVA CVA Exam - Topic 3 Question 55 Discussion

Actual exam question for NACVA's CVA exam
Question #: 55
Topic #: 3
[All CVA Questions]

An intangible's deficiencies are considered curable when the prospective economic benefit of enhancing or modifying it exceeds the current cost (in terms of material, labor, and time) to change it. An intangible's deficiencies are considered incurable when:

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Suggested Answer: B

by Joni at Aug 02, 2022, 01:47 PM

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Rodolfo
4 months ago
C is interesting, but I still lean towards B.
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Devorah
4 months ago
I thought it was A at first, but B really does fit better.
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Nieves
5 months ago
Wait, are we sure about this? Seems too straightforward.
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Glen
5 months ago
Totally agree with B! Makes perfect sense.
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Ronny
5 months ago
Definitely B, costs must outweigh benefits for it to be incurable.
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Destiny
5 months ago
I vaguely recall that option A talks about costs not exceeding benefits, which doesn't fit the definition of incurable. So, B seems more likely.
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Ellen
5 months ago
I could be wrong, but I thought incurable meant that the costs can't be justified by the future benefits, which sounds like option B again.
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Rickie
5 months ago
I'm not entirely sure, but I feel like we discussed something about costs exceeding benefits in a similar practice question.
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Danilo
5 months ago
I think I remember that incurable deficiencies mean the costs outweigh the benefits, so maybe it's B?
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Shawn
5 months ago
Hmm, I'm a bit unsure about this one. I know Archer is a risk management solution, but I'm not sure if it's a fully low-code platform or if it requires some coding. I'll have to think this through carefully.
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Lacey
5 months ago
Okay, let me think this through. Interest rate anticipation and yield curve anticipation are definitely principal investment strategies, so those are out. Sector allocation seems like it could be one too. I'm going to go with "None of these" as my best guess.
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Rodolfo
5 months ago
I'm pretty confident the answer is C - the ROC curve is the best visualization to use when evaluating precision for a binary classification model.
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