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Google Exam Professional Data Engineer Topic 2 Question 66 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 66
Topic #: 2
[All Professional Data Engineer Questions]

Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

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Suggested Answer: C

by Gracia at Mar 01, 2023, 03:15 PM

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Teri
20 days ago
Well, if all else fails, I guess we could just throw a bunch of random values in a hat and pick one. That's how I usually solve my TensorFlow problems!
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Mayra
22 days ago
Ooh, I've heard of sparse_column_with_keys before. That could be an interesting option too. Might have to do some more research on that one.
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Eric
24 days ago
Hold up, what about option C? Isn't that specifically designed for handling unknown values? Seems like the logical choice to me.
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Ty
1 months ago
Hmm, I'm not sure about that. Shouldn't we be using something like categorical_column_with_vocabulary_list instead? That seems more straightforward.
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Pura
6 days ago
I think you're right, categorical_column_with_vocabulary_list is the way to go.
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Tamekia
1 months ago
I think option B is the way to go. Using a hash bucket is a great way to handle unknown values in a categorical column.
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Wynell
7 days ago
I prefer option B as well, using a hash bucket seems like a flexible solution.
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Ilene
25 days ago
I think option A could also work well if you have a list of possible values for the column.
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Jennie
27 days ago
I agree, option B is a good choice for handling unknown values in a categorical column.
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Jeanice
2 months ago
I'm not sure, but I think it's either A or C. We can use categorical_column_with_unknown_values if we don't know all possible values.
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Keneth
2 months ago
I agree with Sabrina, because it allows us to specify a list of possible values for the column.
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Sabrina
2 months ago
I think the answer is A) categorical_column_with_vocabulary_list.
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