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Salesforce ANC-301 Exam - Topic 5 Question 62 Discussion

A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.What should the consultant do?
B) Apply the predict missing values transformation in recipe nodes.
A) Fill in the missing data with an average of all incomes.
C) Drop the field as it will be difficult to get future respondents.

Salesforce ANC-301 Exam - Topic 5 Question 62 Discussion

Actual exam question for Salesforce's ANC-301 exam
Question #: 62
Topic #: 5
[All ANC-301 Questions]

A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.

What should the consultant do?

Show Suggested Answer Hide Answer
Suggested Answer: B

In CRM Analytics, when dealing with incomplete data, specifically when certain respondents have not filled out fields like income, the Predict Missing Values transformation in a recipe is highly effective. This transformation allows you to predict values for missing fields based on patterns from the existing data. Since the consultant finds this field informative despite having data from only 1% of respondents, applying this transformation can estimate these missing values, which ensures that the dataset remains useful for predictive purposes without discarding important variables. Reference: CRM Analytics Recipes and Predict Missing Values


by Queenie at Nov 21, 2025, 07:21 AM

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Lashawn
2 months ago
I feel like B gives us a chance to use the data effectively.
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Lili
2 months ago
True, but if it's only 1%, is it worth it?
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Sharen
2 months ago
But income can be crucial for predictions!
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Reuben
2 months ago
C might be the best choice. It's too sparse.
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Nettie
2 months ago
I prefer B. Predicting values seems smarter.
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Merlyn
3 months ago
Predicting missing values sounds risky, might mislead.
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Tori
3 months ago
Agree, better to drop it than rely on bad data.
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Ammie
3 months ago
Surprised that only 1% shared income info, that's low!
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Moon
4 months ago
I think dropping it makes sense, too few responses!
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Alica
4 months ago
Haha, maybe the respondents are trying to hide their secret wealth! But in all seriousness, B seems like the best choice here.
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Sharika
4 months ago
I'd go with B. Predicting the missing values is more statistically sound than just using the average. Though I do wonder why the income question is so unpopular...
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Simona
4 months ago
Hmm, I'm not sure. Option A seems like a quick fix, but B might be more accurate. Either way, that 1% response rate is pretty low.
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Bernardine
4 months ago
Definitely C. If only 1% of respondents are providing income data, it's not worth keeping that field. Might as well drop it.
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Brice
4 months ago
I think option B is the way to go. Predicting the missing values is a better approach than just filling in with the average.
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Lashawn
5 months ago
I recall a practice question where we had to decide on handling missing values, and I think the predictive method was favored over just dropping the column.
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Joseph
5 months ago
Dropping the income field might seem like a safe option, but I feel like it could still hold some value if we can find a way to estimate the missing data.
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Teddy
5 months ago
I think using a predictive model to fill in missing values could be a better approach, but I'm not entirely sure how accurate that would be with just 1% of respondents.
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Mozell
5 months ago
I'm feeling pretty confident about this one. I think option B is the way to go. Predicting the missing values is going to give me a more accurate model than just using the average or dropping the field. I'll make sure to document my approach clearly in my answer.
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Avery
5 months ago
This is a tricky one. I'm leaning towards option B - using the predict missing values transformation. That seems like the most robust way to handle the missing data, rather than just filling it in or dropping the field entirely. I'll make sure to explain my reasoning on that.
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Stevie
5 months ago
I remember we discussed how filling in missing data with averages can skew results, especially if the sample size is so small.
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Salome
6 months ago
Okay, I think I've got a strategy here. The "predict missing values" transformation sounds like the best approach to me. That way I can try to estimate the missing incomes based on the other demographic data, without just using a simple average.
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Frederica
6 months ago
Filling in with an average could skew results.
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Carman
6 months ago
I think option A is too simplistic.
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Emilio
6 months ago
Hmm, I'm not sure about this one. Filling in the missing data with the average seems like it could skew the results, but dropping the field might mean losing out on some valuable information. I'll have to think this through carefully.
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Jutta
7 months ago
I'm a bit confused on this one. Should I just fill in the missing data with the average income? Or is there a better way to handle the missing values?
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Jacob
29 days ago
It’s tricky, but I’d lean towards predicting the missing values.
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Jerry
1 month ago
I agree, using a transformation could be more effective.
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Ethan
1 month ago
What about using predictive modeling to estimate those missing values?
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Jaleesa
2 months ago
I think filling in with the average is too simplistic.
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Kristal
6 months ago
Dropping the field might lose valuable insights.
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