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Exam Professional Machine Learning Engineer topic 1 question 106 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 106
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

  • A. Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.
  • B. Create a new view with BigQuery that does not include a column with city information
  • C. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.
  • D. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by hiromi at Dec. 20, 2022, 10:03 p.m.

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fitri001
6 months ago
Selected Answer: D
A. Using TensorFlow: This is an overkill for this scenario. BigQuery ML can handle one-hot encoding natively within Dataprep. B. Excluding City Information: This removes a potentially important predictive variable, reducing model accuracy. C. Assigning Region Labels: This approach loses granularity and might not capture the specific variations between cities.
upvoted 3 times
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andresvelasco
1 year, 1 month ago
Selected Answer: D
D by elimination but ... Does not bigquery automatically do one-hot encoding of categorical features for you? Also the wording of the question does not seem right: a linear regression model to predict the likelihodd that the customer ... isnt that a classification model?
upvoted 1 times
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M25
1 year, 5 months ago
Selected Answer: D
Went with D
upvoted 1 times
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Yajnas_arpohc
1 year, 7 months ago
Is it correct to say that A is technically a better way to do things if the ask wast for separate columns?
upvoted 1 times
tavva_prudhvi
1 year, 7 months ago
"least amount of coding"
upvoted 4 times
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guilhermebutzke
1 year, 7 months ago
Selected Answer: D
One-hot is a good way to use categorical variables in regressions problems https://academic.oup.com/rheumatology/article/54/7/1141/1849688 https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-auto-preprocessing
upvoted 3 times
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TNT87
1 year, 7 months ago
Selected Answer: D
Answer D
upvoted 1 times
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abneural
1 year, 8 months ago
Selected Answer: C
for a fuller answer, D--> transforms “state” column not city column C--> at least works with city column
upvoted 1 times
tavva_prudhvi
1 year, 7 months ago
Read smarques comment
upvoted 1 times
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John_Pongthorn
1 year, 8 months ago
Selected Answer: D
https://docs.trifacta.com/display/SS/Prepare+Data+for+Machine+Processing
upvoted 2 times
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smarques
1 year, 9 months ago
Selected Answer: D
This will allow you to maintain the city name variable as a predictor while ensuring that the data is in a format that can be used to train a linear regression model on BigQuery ML.
upvoted 1 times
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Abhijat
1 year, 10 months ago
Selected Answer: D
Answer D
upvoted 1 times
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Abhijat
1 year, 10 months ago
Answer is D
upvoted 1 times
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mymy9418
1 year, 10 months ago
Selected Answer: D
one-hot encoding makes sense to me
upvoted 2 times
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hiromi
1 year, 10 months ago
Selected Answer: C
I vote for C
upvoted 2 times
hiromi
1 year, 10 months ago
Changing my vote to D
upvoted 3 times
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