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Amazon AIF-C01 Exam - Topic 2 Question 17 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 17
Topic #: 2
[All AIF-C01 Questions]

An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.

What should the firm do when developing and deploying the LLM? (Select TWO.)

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Suggested Answer: B

by Simona at Jan 11, 2025, 10:23 AM

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Portia
3 months ago
Prompt engineering might be useful, but not a top priority.
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Emilio
3 months ago
Overfitting is a big no-no, for sure!
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Shayne
3 months ago
Wait, can adjusting the temperature really help with bias?
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Wilson
4 months ago
I think modifying training data is crucial too.
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Shonda
4 months ago
Definitely need to include fairness metrics!
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Quiana
4 months ago
I recall a practice question where we focused on avoiding overfitting. It might not directly relate to responsible deployment, but it’s still important for model performance.
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Vallie
4 months ago
I feel like modifying the training data to mitigate bias is crucial. It aligns with what we practiced in our case studies, but I’m torn between A and C for the second option.
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Johnetta
4 months ago
I’m not entirely sure about the temperature parameter. I think it’s more about controlling creativity in responses rather than addressing potential harms.
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Caitlin
5 months ago
I remember we discussed the importance of fairness metrics in our last class. It seems like A and C could be good choices for ensuring responsible deployment.
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Felix
5 months ago
Prompt engineering, huh? That's an interesting one. I wonder how that would apply to responsible AI development. I'll make sure to consider that as a possibility.
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Melodie
5 months ago
Adjusting the temperature parameter? I'm not sure what that means in this context. I'll have to research that a bit more before deciding. The other options make more sense to me.
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Marcos
5 months ago
Okay, let's see. Fairness metrics and mitigating bias are definitely important. I'm not as sure about the other options, but I'll give it my best shot.
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Dong
5 months ago
Hmm, I'm a bit unsure about this one. There are a lot of options, and I'm not sure which two are the most important. I'll have to think it through carefully.
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Joye
5 months ago
This seems like a straightforward question about responsible AI development. I'm confident I can identify the key steps the firm should take.
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Jaime
10 months ago
Adjusting the temperature parameter? Is this model going to be serving up hot takes or crunching numbers?
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Gearldine
9 months ago
Consider the ethical implications of automating document processing.
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Ahmad
9 months ago
Implement safeguards to prevent bias and misinformation.
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Katie
9 months ago
Ensure the LLM is trained on diverse and representative data.
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Lynna
10 months ago
Prompt engineering, huh? Sounds like they're trying to train the model to be a poet. As long as it can crunch those numbers, I'm good.
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Nathan
9 months ago
E) Apply prompt engineering techniques.
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Bernardine
9 months ago
C) Modify the training data to mitigate bias.
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Jeannetta
10 months ago
A) Include fairness metrics for model evaluation.
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Andra
11 months ago
I believe modifying the training data to mitigate bias is also crucial in this case.
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Lili
11 months ago
Whoa, hold up! Modifying the training data? Isn't that like cheating on your homework? I hope they know what they're doing.
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Kelvin
10 months ago
E) Apply prompt engineering techniques.
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Valentin
10 months ago
C) Modify the training data to mitigate bias.
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Mirta
10 months ago
A) Include fairness metrics for model evaluation.
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Raina
11 months ago
Adjusting the temperature parameter? What is this, a cooking class? I'd rather they focus on avoiding overfitting and applying prompt engineering.
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Tiara
11 months ago
Definitely need to include fairness metrics and modify the training data. Bias is a big concern with these models, so they gotta do it right.
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Tayna
11 months ago
I agree with Aimee. It's important to ensure the model is fair and unbiased.
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Aimee
11 months ago
I think the firm should include fairness metrics for model evaluation.
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