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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 43 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 43
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

  • A. Thee individual features: binned latitude, binned longitude, and one-hot encoded car type.
  • B. One feature obtained as an element-wise product between latitude, longitude, and car type.
  • C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.
  • D. Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by ralf_cc at July 9, 2021, 8:55 a.m.

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Paul_Dirac
Highly Voted 3 years, 3 months ago
C https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
upvoted 22 times
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ebinv2
Highly Voted 3 years, 4 months ago
C should be the answer
upvoted 8 times
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desertlotus1211
Most Recent 1 month ago
Why not Answer D?
upvoted 1 times
desertlotus1211
1 month ago
Would C be moe complex than D?
upvoted 1 times
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baimus
2 months, 2 weeks ago
While i acknowledge the answer is C, It seems wrong to elementwise combine binned lat/lon, as it means there are at least 2 places with the same number in the world, probably more. Not only but by multiplying the binned a values it implies they are ordinal, but they are not ordinal in the same direction, so the relationship on price will be lost (a good example is northern countries tend to be richer, but the east/west relationship isn't defined)
upvoted 2 times
desertlotus1211
1 month ago
Why not Answer D?
upvoted 1 times
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desertlotus1211
1 month ago
Would C be moe complex than D?
upvoted 1 times
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PhilipKoku
5 months, 2 weeks ago
Selected Answer: C
C) one feature
upvoted 1 times
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Sum_Sum
1 year ago
C - everything else is madness
upvoted 2 times
rigori
4 months, 3 weeks ago
creating this cross feature is madness from explainability standpoint
upvoted 1 times
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M25
1 year, 6 months ago
Selected Answer: C
Went with C
upvoted 1 times
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Mohamed_Mossad
2 years, 5 months ago
Selected Answer: C
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
upvoted 4 times
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A4M
2 years, 10 months ago
C - Answer when doing feature cross the features need to be binned
upvoted 4 times
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MK_Ahsan
2 years, 10 months ago
Selected Answer: C
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding Answer C: It needs a feature cross to obtain one feature.
upvoted 3 times
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NamitSehgal
2 years, 10 months ago
I got with C
upvoted 3 times
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ramen_lover
3 years ago
"element-wise product" sounds like we are not using a feature cross but artificially creating a new column whose values is the "element-wise product" of other column values...; i.e., (1, 2, 3) => 1 * 2 * 3 = 6. I am not a native English speaker; thus, I might misunderstand the sentence.
upvoted 1 times
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ralf_cc
3 years, 4 months ago
D - https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
upvoted 4 times
jk73
3 years, 2 months ago
Cannot be D, Despite Binning is a good idea because it enables the model to learn nonlinear relationships within a single feature; separate latitude and longitude in different feature crosses is not a good one, this separation will prevent the model from learning city-specific sales. A city is the conjunction of latitude and longitude. In that order of Ideas Crossing binned latitude with binned longitude enables the model to learn city-specific effects of car type. I will go for C, https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
upvoted 13 times
george_ognyanov
3 years, 1 month ago
Damn that was a good explanation. Thank you for writing it out.
upvoted 2 times
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C (25%)
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