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Salesforce AI Associate Exam - Topic 1 Question 34 Discussion

Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.What is the most appropriate action the company should take?
C) Investigate the data inconsistencies and apply data quality techniques.
A) Adjust the Al model to account for the data inconsistencies.
B) Increase the quantity of data being used for training the model

Salesforce AI Associate Exam - Topic 1 Question 34 Discussion

Actual exam question for Salesforce's Salesforce AI Associate exam
Question #: 34
Topic #: 1
[All Salesforce AI Associate Questions]

Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.

What is the most appropriate action the company should take?

Show Suggested Answer Hide Answer
Suggested Answer: C

When inconsistencies in data are identified, the most appropriate action is to investigate these inconsistencies and apply data quality techniques. Adjusting the AI model to accommodate poor quality data or simply increasing the quantity of data without addressing the underlying issues does not solve the problem and can lead to less reliable AI outputs. Proper data cleaning, normalization, and validation are necessary steps to ensure that the data fed into an AI model is accurate and reliable, thus enhancing the model's performance. Salesforce provides guidelines on how to manage and improve data quality, including practical steps for addressing data inconsistencies, detailed at Improving Data Quality in Salesforce.


by Teddy at Nov 15, 2024, 07:16 AM

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Nobuko
6 months ago
Not sure if investigating will really fix everything...
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Val
6 months ago
Totally agree with C, data quality is key!
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Art
7 months ago
Wait, why not just adjust the model? Seems easier!
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Leeann
7 months ago
I think B could help too, but not as much.
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Margurite
7 months ago
C is definitely the way to go!
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Lauran
7 months ago
Increasing the quantity of data could help, but if the existing data is flawed, it might just amplify the problems. I'm leaning towards option C too.
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Marvel
8 months ago
I feel like we practiced a similar question where investigating data inconsistencies was key. Option C seems like the right approach.
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Kanisha
8 months ago
I'm not entirely sure, but I think just adjusting the model might not solve the underlying issues. It feels like a band-aid solution.
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Adelaide
8 months ago
I remember we discussed how important it is to address data quality before training an AI model, so I think option C makes the most sense.
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Elena
8 months ago
I think increasing the training data quantity, option B, could also help overcome the inconsistencies. But investigating the data issues and fixing them, as in option C, is probably the most thorough approach.
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Elizabeth
8 months ago
Option C is definitely the way to go. Improving the data quality is crucial for building a reliable AI model. I'd focus on identifying the root causes of the inconsistencies and applying appropriate data cleansing techniques.
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Ronald
8 months ago
Hmm, I'm a bit unsure here. Should we just adjust the model to account for the inconsistencies or try to fix the data first? I'm not sure which approach would be better.
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Albina
8 months ago
This seems like a straightforward data quality issue. I'd go with option C and investigate the inconsistencies to clean up the data before training the model.
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Nada
2 years ago
B? Really? Quantity over quality? What is this, a fast food restaurant? No, no, C is the way to go. Gotta clean up that data first before you can even think about training the model.
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Gracie
1 year ago
C? Absolutely, cleaning up the data is crucial for accurate results.
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Lemuel
1 year ago
C) Investigate the data inconsistencies and apply data quality techniques.
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Frank
1 year ago
B) Increase the quantity of data being used for training the model
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Eulah
1 year ago
A) Adjust the AI model to account for the data inconsistencies.
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Tori
2 years ago
A, for sure. Just let the AI model figure it out. That's what machine learning is all about, right? Adjusting and adapting to whatever data you throw at it.
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Derick
2 years ago
Oh, I bet the data is a real mess. Probably just a bunch of cat photos labeled as 'dog' or something. Time to get our data cleaning hats on!
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Queenie
1 year ago
A) Adjust the AI model to account for the data inconsistencies.
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Willard
1 year ago
C) Investigate the data inconsistencies and apply data quality techniques.
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Olive
1 year ago
A) Adjust the AI model to account for the data inconsistencies.
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Viva
2 years ago
I believe adjusting the AI model to account for the data inconsistencies is also important.
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Catarina
2 years ago
C'mon, the obvious choice here is C. Data quality is key for any AI model. You can't just slap some extra data on it and hope for the best.
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Solange
1 year ago
User 4: More data won't help if the existing data is flawed, C is the way to go.
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Benedict
1 year ago
User 3: Adjusting the AI model won't solve the root problem, we need to investigate and apply data quality techniques.
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Rosina
2 years ago
User 2: Definitely, without addressing the data inconsistencies, the model won't be accurate.
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Genevieve
2 years ago
User 1: I agree, data quality is crucial for the success of the AI model.
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Tamesha
2 years ago
I agree with Yvonne. Applying data quality techniques is crucial for accurate results.
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Yvonne
2 years ago
I think the company should investigate the data inconsistencies.
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