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Oracle Exam 1Z0-1110-23 Topic 5 Question 23 Discussion

Actual exam question for Oracle's 1Z0-1110-23 exam
Question #: 23
Topic #: 5
[All 1Z0-1110-23 Questions]

3. When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) Data

Science model catalog, you create a score.py file. What is the purpose of the score.py file?

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Suggested Answer: A

by Amira at Jul 02, 2024, 01:11 PM

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Dustin
9 months ago
Wow, this is a tough one. I'm torn between B) auto_transform() and C) OneHotEncoder(). Maybe I should just flip a coin... or better yet, just ask the cat. They always have the answers.
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Ressie
9 months ago
D) show_in_notebook()? Really? I think that's more for visualizing data than encoding categorical features. C) OneHotEncoder() is the clear winner here. It's like the Swiss Army knife of categorical encoding.
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Bulah
9 months ago
Ah, the age-old dilemma of categorical feature encoding. I'd have to say C) OneHotEncoder() is the way to go. It may be a bit more work, but it ensures you don't lose any valuable information. Plus, it's way more fun to say than DataFramLabelEncode().
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Kristofer
9 months ago
Hmm, I'm not so sure about that. B) auto_transform() sounds like it might be a handy shortcut, but I'd rather have more control over the encoding process. C) OneHotEncoder() is the way to go for this use case.
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India
8 months ago
OneHotEncoder() seems like the most suitable choice for this use case.
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Ona
8 months ago
Yeah, I prefer having more control over the encoding process as well.
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Ruby
8 months ago
I agree, it provides more control over the encoding process.
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Paris
8 months ago
I think C) OneHotEncoder() is the best option for encoding categorical features.
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Mozelle
10 months ago
I'm not sure, but I think OneHotEncoder creates binary columns for each category
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Glory
10 months ago
I agree with Ayesha, OneHotEncoder is commonly used for encoding categorical features
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Ayesha
10 months ago
I think the answer is C) OneHotEncoder()
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Rosendo
10 months ago
I'd go with C) OneHotEncoder(). It's the perfect choice for encoding multiple categorical features without losing information. Plus, it's a classic go-to in the data science world.
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Nida
9 months ago
I think OneHotEncoder() is the most efficient option for encoding multiple categorical features in this case.
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Marge
9 months ago
I agree, OneHotEncoder() is definitely the way to go for encoding categorical features.
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