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Google Professional Machine Learning Engineer Exam - Topic 2 Question 26 Discussion

You are an ML engineer at a global car manufacturer. 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?
C) One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
A) Three 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
D) Two feature crosses as a 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

Google Professional Machine Learning Engineer Exam - Topic 2 Question 26 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 26
Topic #: 2
[All Professional Machine Learning Engineer Questions]

You are an ML engineer at a global car manufacturer. 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?

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Suggested Answer: C

by Gladys at May 03, 2022, 03:38 PM

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Cyril
10 months ago
Wait, can one feature really capture all that info? Sounds off.
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Selene
10 months ago
A is too simple, we need more granularity.
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Devon
11 months ago
Not sure about D, seems a bit overcomplicated.
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Cyndy
11 months ago
Definitely agree with C, binned features are key!
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Ressie
11 months ago
I think option C makes the most sense for city-specific predictions.
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Curtis
11 months ago
I practiced a similar question where we combined features, and I think option C sounds right because it includes both binned latitude and longitude with one-hot encoding.
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Laquita
11 months ago
I'm not entirely sure, but I feel like using one-hot encoding for car type is essential. It might help with the categorical nature of the data.
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Madelyn
11 months ago
I remember we discussed feature engineering in class, and I think binned latitude and longitude could help capture regional trends.
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Belen
11 months ago
I’m a bit confused about the element-wise product approach. I wonder if option D could be more effective since it considers interactions between features.
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Elin
11 months ago
Ah, this is a tricky one. I'm torn between B and D. Removing the SD for the target behavior could also be considered a form of extinction, but withholding the maintaining reinforcer seems like the more direct approach. I'll have to weigh the pros and cons of each.
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Melda
11 months ago
Okay, this looks like a future value calculation with varying inflation rates. I'll need to calculate the future car cost first.
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Zack
11 months ago
Okay, I think I've got this. Clinical practice guidelines are a utilization management tool to help providers determine the best course of treatment. I'm going with answer B.
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Latia
11 months ago
Hmm, this is a tricky one. I'm not entirely sure about the difference between a put option and a call option. I'll have to think this through carefully before selecting an answer.
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