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Salesforce AI Associate Exam - Topic 3 Question 8 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam

Question #: 8
Topic #: 3

[All Salesforce AI Associate Questions]

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

AAI model training becomes slower and less efficient

BAI models may produce biased or erroneous results.

CAI models become more interpretable

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 Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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

by Jettie at Nov 25, 2023, 12:36 AM

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Polly

6 months ago

C seems off, bad data can't make models clearer, right?

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Lorenza

6 months ago

A is true, but B is the bigger issue.

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Shantell

7 months ago

Wait, how can data make models slower? Sounds weird.

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Eura

7 months ago

I agree, poor data leads to poor outcomes for sure.

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Willie

7 months ago

Definitely B, biased results are a huge risk!

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Davida

7 months ago

I’m a bit confused about the options, but I don’t think poor-quality data would make models more interpretable. That doesn’t seem right to me.

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Azalee

8 months ago

I recall a practice question that mentioned how poor data could lead to errors in AI outputs. I think that's a strong possibility here.

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Argelia

8 months ago

I'm not entirely sure, but I feel like using bad data might slow down the training process too. It seems like it could affect efficiency somehow.

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Dorthy

8 months ago

I think poor-quality data can definitely lead to biased results, like we discussed in class. I remember a case study that highlighted this issue.

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Sabine

8 months ago

I've got a good feeling about this one. Based on what we've learned, I'm pretty sure that the correct answer is B - poor-quality data can lead to biased or erroneous results in AI models. That seems like the most logical outcome.

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Nichelle

8 months ago

I'm a bit confused by this question. I know that data quality is important for AI, but I'm not sure if the other options are completely wrong. I'll have to review my notes and think about it some more.

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Emilio

8 months ago

Hmm, I'm a little unsure about this one. I know that data quality is important for AI, but I'm not sure if the other options are completely wrong. I'll have to think it through carefully.

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Tracey

8 months ago

This one seems pretty straightforward. I'm pretty confident that the correct answer is B - poor-quality data can lead to biased or erroneous results in AI models.

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Felix

8 months ago

Okay, let's see. I remember from the lectures that poor-quality data can definitely lead to biased or incorrect outputs from AI models. I think B is the best answer here.

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Rosina

8 months ago

I'm a little confused by the options here. I'm not sure how a force field analysis or a decision matrix would be relevant for this situation. I think I'll have to eliminate those and focus on the flowchart and check sheet.

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Dan

8 months ago

I'm a bit confused on this one. Is the UE also a key component that needs to be upgraded? I'm not sure if that's required for URLLC or not.

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Oretha

8 months ago

I feel like I've seen something similar where S corporation income was excluded, so maybe it's just the $50,000?

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