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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?

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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 Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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.''


by Beckie at May 07, 2024, 03:20 AM

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Johana
7 months ago
Good point, Eve. Erroneous results can have more impact than just slow training.
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Lashon
7 months ago
True, but I feel biases in results are a bigger issue. Errors can lead to bigger consequences.
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Nieves
7 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
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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Princess
6 months ago
B) AI models may produce biased or erroneous results.
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Frankie
7 months ago
A) AI model training becomes slower and less efficient
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Douglass
7 months ago
I agree, but what about A? Slow and less efficient training could be a problem too.
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Ronnie
7 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
7 months ago
It's important to ensure the data used for training AI models is of high quality.
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Eladia
7 months ago
I agree, AI models need good data to make accurate predictions.
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Ty
7 months ago
Yeah, using poor-quality data can definitely lead to biased or erroneous results.
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Carylon
8 months ago
Makes sense, B seems logical. Poor quality usually messes up results.
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Jaime
8 months ago
Yeah, definitely. I'm leaning towards answer B, AI models may produce biased or erroneous results.
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Alana
8 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
7 months ago
B) AI models may produce biased or erroneous results.
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Linn
7 months ago
A) AI model training becomes slower and less efficient
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Natalya
7 months ago
B) AI models may produce biased or erroneous results.
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Cornell
7 months ago
B) AI models may produce biased or erroneous results.
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Patria
7 months ago
A) AI model training becomes slower and less efficient
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Ma
7 months ago
A) AI model training becomes slower and less efficient
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Hubert
8 months ago
I agree with Jutta. Garbage in, garbage out, as they say. B is the correct choice here.
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Norah
7 months ago
B) AI models may produce biased or erroneous results.
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Margurite
7 months ago
I agree with Jutta. Garbage in, garbage out, as they say.
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Jutta
8 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
7 months ago
B) AI models may produce biased or erroneous results.
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Shenika
7 months ago
That's right, poor-quality data can have a negative impact on AI model outcomes.
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Amie
7 months ago
B) AI models may produce biased or erroneous results.
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Amina
7 months ago
A) AI model training becomes slower and less efficient
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Tiera
7 months ago
Yes, using poor-quality data can definitely lead to biased or erroneous results.
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Emilio
7 months ago
B) AI models may produce biased or erroneous results.
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An
8 months ago
A) AI model training becomes slower and less efficient
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Johana
9 months ago
I think the question about the outcome of poor-quality data in AI is pretty important.
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