Salesforce AI Associate Exam - Topic 1 Question 50 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam

Question #: 50
Topic #: 1

[All Salesforce AI Associate Questions]

How does poor data quality affect predictive and generative AI models?

ACreates inaccurate results

BIncreases raw data volume

CDecreases storage efficiency

Show Suggested Answer

Suggested Answer: A

Poor data quality significantly impacts the performance of predictive and generative AI models by leading to inaccurate and unreliable results. Factors such as incomplete data, incorrect data, or poorly formatted data can mislead AI models during the learning phase, causing them to make incorrect assumptions, learn inappropriate patterns, or generalize poorly to new data. This inaccuracy can be detrimental in applications where precision is critical, such as in predictive analytics for sales forecasting or customer behavior analysis. Salesforce emphasizes the importance of data quality for AI model effectiveness in their AI best practices guide, which can be reviewed on Salesforce AI Best Practices.

by Georgene at Nov 20, 2025, 02:20 PM

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Miss

5 days ago

A is the biggest issue. Trust in AI relies on quality data.

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Kimberely

10 days ago

C) Decreases storage efficiency. Waste of resources!

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Hollis

15 days ago

B) Increases raw data volume. More data, more noise!

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Adrianna

20 days ago

Totally agree! Bad data leads to wrong predictions.

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Lai

25 days ago

A) Creates inaccurate results. It's crucial for AI.

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Dustin

1 month ago

C) Decreases storage efficiency? Didn't see that coming!

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Reyes

1 month ago

B) Increases raw data volume, that's a big issue!

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Lavera

2 months ago

Really? I thought AI could handle messy data better.

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Haydee

2 months ago

Totally agree, bad data = bad predictions.

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Royce

2 months ago

A) Creates inaccurate results for sure!

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Lenna

2 months ago

Haha, B) Increases raw data volume. That's a good one, but not the right answer.

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Wendell

3 months ago

C) Decreases storage efficiency? I don't think that's the main issue here.

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Tasia

3 months ago

A) for sure. Garbage in, garbage out, as they say.

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Alethea

3 months ago

B) Increases raw data volume? Seriously? How is that relevant?

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Laurel

3 months ago

I think the main issue is definitely the inaccurate results, but I wonder if the other options could also be related in some way. It’s a bit confusing!

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Major

3 months ago

I feel like we had a practice question about storage efficiency, and it mentioned that poor data quality could decrease it. But I'm not completely confident about that.

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Karan

3 months ago

I remember we discussed how poor data quality can lead to inaccurate results, especially in predictive models. That seems like the most straightforward impact.

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Alberta

4 months ago

I'm feeling pretty confident about this one. Poor data quality reduces the reliability and performance of both predictive and generative AI models. Gotta nail that in my answer.

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Temeka

4 months ago

I think the key here is understanding how poor data quality affects the training process for these models. I'll need to explain how it can introduce bias and errors.

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Zack

4 months ago

Okay, I've got this. Poor data quality leads to inaccurate results in both predictive and generative models. That's the key thing to focus on for this question.

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Samuel

4 months ago

A) Creates inaccurate results. That's the obvious one.

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Iluminada

5 months ago

I'm not entirely sure, but I think poor data quality might also increase raw data volume because you end up with a lot of irrelevant or redundant data.

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Cassandra

5 months ago

Hmm, I'm a bit unsure about this. I know poor data quality is bad, but I'm not sure of the specific effects on predictive and generative models. I'll have to review my notes.

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