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Salesforce Exam Salesforce AI Associate Topic 1 Question 18 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 18
Topic #: 1
[All Salesforce AI Associate Questions]

What is a potential outcome of using poor-quality data in AI application?

Show Suggested Answer Hide Answer
Suggested Answer: B

''A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting.''


Contribute your Thoughts:

Johana
6 months ago
Good point, Eve. Erroneous results can have more impact than just slow training.
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Lashon
6 months ago
True, but I feel biases in results are a bigger issue. Errors can lead to bigger consequences.
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Nieves
6 months ago
I'm just hoping the AI doesn't start generating its own poor-quality data. That would be a real plot twist!
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Jennie
5 months ago
I'm just hoping the AI doesn't start generating its own poor-quality data. That would be a real plot twist!
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Princess
5 months ago
B) AI models may produce biased or erroneous results.
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Frankie
6 months ago
A) AI model training becomes slower and less efficient
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Douglass
6 months ago
I agree, but what about A? Slow and less efficient training could be a problem too.
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Ronnie
6 months ago
Haha, I bet the AI models would start producing some real 'alternative facts' with poor data. Option B all the way!
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Twila
5 months ago
It's important to ensure the data used for training AI models is of high quality.
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Eladia
6 months ago
I agree, AI models need good data to make accurate predictions.
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Ty
6 months ago
Yeah, using poor-quality data can definitely lead to biased or erroneous results.
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Carylon
6 months ago
Makes sense, B seems logical. Poor quality usually messes up results.
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Jaime
6 months ago
Yeah, definitely. I'm leaning towards answer B, AI models may produce biased or erroneous results.
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Alana
7 months ago
Hmm, I'm not so sure. Maybe C? If the data is poor, the models might become more interpretable as a way to understand what's going on. Just a thought.
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Louisa
5 months ago
B) AI models may produce biased or erroneous results.
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Linn
6 months ago
A) AI model training becomes slower and less efficient
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Natalya
6 months ago
B) AI models may produce biased or erroneous results.
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Cornell
6 months ago
B) AI models may produce biased or erroneous results.
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Patria
6 months ago
A) AI model training becomes slower and less efficient
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Ma
6 months ago
A) AI model training becomes slower and less efficient
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Hubert
7 months ago
I agree with Jutta. Garbage in, garbage out, as they say. B is the correct choice here.
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Norah
6 months ago
B) AI models may produce biased or erroneous results.
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Margurite
6 months ago
I agree with Jutta. Garbage in, garbage out, as they say.
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Jutta
7 months ago
Option B seems like the most logical answer. Poor-quality data can definitely lead to biased or erroneous results from AI models.
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Kristal
5 months ago
B) AI models may produce biased or erroneous results.
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Shenika
6 months ago
That's right, poor-quality data can have a negative impact on AI model outcomes.
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Amie
6 months ago
B) AI models may produce biased or erroneous results.
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Amina
6 months ago
A) AI model training becomes slower and less efficient
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Tiera
6 months ago
Yes, using poor-quality data can definitely lead to biased or erroneous results.
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Emilio
6 months ago
B) AI models may produce biased or erroneous results.
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An
6 months ago
A) AI model training becomes slower and less efficient
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Johana
7 months ago
I think the question about the outcome of poor-quality data in AI is pretty important.
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