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Databricks Machine Learning Professional Exam - Topic 10 Question 11 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 11
Topic #: 10
[All Databricks Machine Learning Professional Questions]

Which of the following is a simple statistic to monitor for categorical feature drift?

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Suggested Answer: C

by Wilda at Mar 15, 2024, 06:56 PM

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Kendra
3 months ago
Percentage of missing values is also important, but not the only one.
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Dominga
3 months ago
Totally agree with C, it gives a fuller picture!
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Tommy
3 months ago
Wait, can you really just use the mode for feature drift? Seems too simple.
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Margo
4 months ago
I think option C covers more ground, though.
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Celestina
4 months ago
The mode is definitely a key statistic for categorical data!
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Alesia
4 months ago
I vaguely remember that the percentage of missing values can indicate drift, but I'm not confident if it's the best choice here.
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Winfred
4 months ago
I feel like the answer might be C since it includes multiple aspects of monitoring, but I can't recall if all of them are necessary.
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Bettina
4 months ago
I think we practiced a question about feature drift, and the mode was definitely mentioned as important.
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Janine
5 months ago
I remember we discussed the mode as a key statistic for categorical data, but I'm not sure if it's the only one we should focus on.
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Stephaine
5 months ago
I'd go with the percentage of missing values. That's a simple but important metric to keep an eye on, as changes there could indicate issues with data collection or processing.
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Thad
5 months ago
The mode, number of unique values, and percentage of missing values seem like a good set of statistics to track. That would give you a pretty comprehensive view of any changes in the categorical feature over time.
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Trinidad
5 months ago
I'm a bit unsure about this one. I know the mode is a measure of central tendency, but I'm not sure if it's the best for monitoring drift. Maybe the number of unique values or percentage of missing values would be better?
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Mammie
5 months ago
I think the mode is a good simple statistic to monitor for categorical feature drift. It gives a sense of the most common value, which could change over time.
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Verdell
5 months ago
I'm leaning towards the number of unique values as the best option here. Tracking how the diversity of the feature changes over time could be a useful way to detect drift.
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Frederica
5 months ago
This is a tricky one. There are a few options that seem relevant, but I'm not sure which one is the best fit. I'll need to think through the requirements step-by-step to make sure I select the right answer.
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Dawne
5 months ago
I'm a bit unsure about the third one, 'not' not in 'in'. I'll need to double-check the logic on that one.
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Tammara
10 months ago
Hmm, I wonder if the mode is the most suitable statistic for monitoring categorical feature drift. Maybe we need to consider the number of unique values and the percentage of missing values too. Just a thought.
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Lacey
8 months ago
It's good to have multiple statistics to monitor for a more comprehensive analysis.
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Mi
8 months ago
I agree, the mode alone may not be enough to monitor categorical feature drift.
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Viki
9 months ago
I think considering the number of unique values and percentage of missing values is important too.
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Lorean
10 months ago
The mode? Really? I thought we were supposed to monitor the number of unique values and missing values. This is a no-brainer, folks. Option C is the way to go.
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Jackie
9 months ago
Let's make sure to keep an eye on the number of unique values and missing values to catch any drift early on.
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Katy
9 months ago
Yeah, option C seems like the most comprehensive choice to monitor categorical feature drift.
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Brittani
10 months ago
I think the mode can also be useful in some cases, but overall, option C covers all the important statistics to monitor.
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Tamekia
10 months ago
I agree, monitoring the number of unique values and missing values is crucial for detecting drift.
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Fletcher
10 months ago
Option C covers all the bases. Monitoring those three statistics should give a good indication of any changes in the categorical feature over time.
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Bobbye
10 months ago
I'm not sure if the mode alone is enough. Checking the number of unique values and percentage of missing values as well seems like a more comprehensive approach.
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Kristeen
10 months ago
The mode seems like a good starting point to monitor for categorical feature drift. I wonder if the number of unique values and percentage of missing values could also provide useful insights.
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Nydia
9 months ago
Yes, keeping an eye on the number of unique values and percentage of missing values can also help us detect any drift in the categorical feature.
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Ena
10 months ago
I agree, monitoring the mode can give us a good idea of any changes in the most frequent category.
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Nieves
11 months ago
But monitoring the percentage of missing values is also important to detect drift in categorical features.
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My
11 months ago
I disagree, I believe the answer is E) Number of unique values.
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Nieves
11 months ago
I think the answer is C) Mode, number of unique values, and percentage of missing values.
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