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Google Professional Machine Learning Engineer Exam - Topic 10 Question 50 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 50
Topic #: 10
[All Professional Machine Learning Engineer Questions]

You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

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Suggested Answer: B

https://cloud.google.com/bigquery-ml/docs/introduction


by Yuki at Aug 04, 2022, 01:45 AM

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Anglea
4 months ago
Custom DNN sounds like overkill for this task.
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Gertude
4 months ago
Definitely leaning towards option B, it’s proven!
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Thora
5 months ago
Wait, can RMSLE really handle negative target values?
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Talia
5 months ago
I think AutoML Tables is the way to go here.
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Dorsey
5 months ago
BQML XGBoost is super efficient for large datasets!
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Wilda
5 months ago
Creating a custom TensorFlow model sounds complex for this task. I think leveraging AutoML might be more efficient given the dataset size.
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Gladys
5 months ago
I feel like using RMSLE could be beneficial since the target can have negative values, but I’m not confident about the specifics of that optimization.
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Laila
5 months ago
I’m not entirely sure, but I think AutoML could save time. I just can’t recall if early stopping is necessary for this scenario.
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Renea
5 months ago
I remember we discussed using BQML for regression tasks, especially with large datasets. XGBoost seems like a solid choice for performance.
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Kaitlyn
5 months ago
I'm pretty confident about this one. DCO seems to provide real-time visibility and better collaboration between business and IT, so I'll go with A and D.
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Kimberely
5 months ago
From what I remember, STDI2E is for imputing missing values, so that could be a viable solution. I'll have to double-check the details though.
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