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Exam Professional Machine Learning Engineer topic 1 question 91 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 91
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company’s profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

  • A. Use latitude, longitude, and product type as features. Use profit as model output.
  • B. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
  • C. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
  • D. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by ares81 at Dec. 14, 2022, 1:39 p.m.

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hiromi
Highly Voted 1 year, 12 months ago
Selected Answer: C
C (not sure) - https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture - https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization
upvoted 7 times
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sonicclasps
Most Recent 10 months, 3 weeks ago
Selected Answer: D
the question asks to predict profitability , not profit. profitability is calculated from revenue and expenses. the correct answer is D
upvoted 2 times
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andresvelasco
1 year, 2 months ago
Most people have chosen C but: Does it make sense to do binning after feature cross? Isnt it the other way around?
upvoted 2 times
maukaba
1 year, 1 month ago
I agree it is the way around. See example: https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding One feature cross: [binned latitude X binned longitude X binned roomsPerPerson]
upvoted 1 times
maukaba
1 year, 1 month ago
In the following examples it is said that it is not possible to cross lat & lon without bucketized them before since continous values must be converted into discrete before crossing : https://www.kaggle.com/code/vikramtiwari/feature-crosses-tensorflow-mlcc
upvoted 1 times
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M25
1 year, 7 months ago
Selected Answer: C
Went with C
upvoted 1 times
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tavva_prudhvi
1 year, 8 months ago
Selected Answer: C
Option C is the best option because it takes into account both the product type and location, which can affect profitability. Binning the feature cross of latitude and longitude can help capture the nonlinear relationship between location and profitability, and using profit as the model output is appropriate because it's the target variable we want to predict.
upvoted 4 times
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abneural
1 year, 10 months ago
Selected Answer: C
Agreeing with hiromi, taxberg Feature cross and bucket lat and lon on geographical problems
upvoted 1 times
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enghabeth
1 year, 10 months ago
Selected Answer: C
your output is profit
upvoted 1 times
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taxberg
1 year, 10 months ago
Selected Answer: C
Must be C. Always feature cross lat and lon on geographical problems. Also, D can not be right as we do not have revenue in the dataset.
upvoted 2 times
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mil_spyro
2 years ago
Selected Answer: A
In this case, there is no need to reduce the number of unique values in the latitude and longitude variables, and binning would reduce information from those features hence A
upvoted 2 times
mil_spyro
2 years ago
binding and crossing*
upvoted 1 times
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hiromi
1 year, 11 months ago
Why no need to reduce?
upvoted 1 times
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ares81
2 years ago
Selected Answer: C
Easy C.
upvoted 2 times
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Community vote distribution
A (35%)
C (25%)
B (20%)
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