PMI CPMAI_v7 Exam - Topic 3 Question 14 Discussion

Actual exam question for PMI's CPMAI_v7 exam

Question #: 14
Topic #: 3

You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results.

What's your best course of action?

AKeep going as planned and the problem will eventually correct itself

BTry to get additional data - at least 5 training examples for each dimension in the representation

CTry to get additional information from project lead to see how many examples per dimension are needed

DTry to improve the quality of your data through more preparation

Show Suggested Answer

Suggested Answer: B

by Nakisha at Jan 05, 2026, 05:16 AM

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Natalie

3 days ago

D) Try to improve the quality of your data through more preparation. Garbage in, garbage out, am I right?

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Mariann

8 days ago

C) Try to get additional information from project lead to see how many examples per dimension are needed. Gotta make sure we have the right requirements.

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Tula

13 days ago

B) Try to get additional data - at least 5 training examples for each dimension in the representation. That's the only way to properly train the model.

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Dulce

18 days ago

I vaguely remember a case study where we had to balance data quality and quantity. I think D might be the best approach, but I’m not entirely confident about the specifics.

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Tracie

24 days ago

I feel uncertain about just pushing through with the current plan. It seems risky, but I can't recall if we covered what to do in such cases. Maybe C could help clarify our needs?

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Shonda

29 days ago

I think we practiced a similar question where we had to decide between getting more data or improving quality. I’m leaning towards D, but I wonder if we should also consider the project lead's input.

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Willie

1 month ago

I remember discussing the importance of having enough examples for each dimension in our training sessions. Option B seems like a solid choice, but I’m not sure if 5 is enough.

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Juliann

1 month ago

I'm not sure keeping going as planned is the best idea here. With too few examples per dimension, the model is likely to overfit and not generalize well. I'll definitely avoid option A.

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Von

1 month ago

I'm feeling pretty confident about this one. I'll try to improve the quality of the data through more preparation. That should help address the dimensionality issues and lead to better predictive performance.

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Oretha

2 months ago

Okay, I've seen issues like this before. My best bet is to try to get more data - at least 5 examples per dimension. That should give me enough to train the systems properly and get reliable results.

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Chantell

2 months ago

Hmm, this is a tricky one. I think I'll try to get additional information from the project lead to understand the minimum number of examples needed per dimension. That seems like the safest approach.

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Telma

2 months ago

I'm a bit confused by this question. I'm not sure how to approach a dataset with high dimensionality and not enough examples per dimension. I'll need to think this through carefully.

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