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?

Acategorical_column_with_vocabulary_list

Bcategorical_column_with_hash_bucket

Ccategorical_column_with_unknown_values

Dsparse_column_with_keys

Show Suggested Answer

Suggested Answer: C

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

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Teri

13 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

14 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

16 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

26 days 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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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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Ilene

17 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

19 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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