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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?

AMode

BNone of these

CMode, number of unique values, and percentage of missing values

DPercentage of missing values

ENumber of unique values

Show Suggested Answer

Suggested Answer: C

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

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Kendra

7 months ago

Percentage of missing values is also important, but not the only one.

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Dominga

8 months ago

Totally agree with C, it gives a fuller picture!

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Tommy

8 months ago

Wait, can you really just use the mode for feature drift? Seems too simple.

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Margo

8 months ago

I think option C covers more ground, though.

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Celestina

8 months ago

The mode is definitely a key statistic for categorical data!

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Alesia

9 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

9 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

9 months ago

I think we practiced a question about feature drift, and the mode was definitely mentioned as important.

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Janine

9 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

9 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

9 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

9 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

9 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

9 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

9 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

10 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

1 year 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

1 year ago

It's good to have multiple statistics to monitor for a more comprehensive analysis.

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Mi

1 year ago

I agree, the mode alone may not be enough to monitor categorical feature drift.

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Viki

1 year ago

I think considering the number of unique values and percentage of missing values is important too.

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Lorean

1 year 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

1 year 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

1 year ago

Yeah, option C seems like the most comprehensive choice to monitor categorical feature drift.

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Brittani

1 year 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

1 year ago

I agree, monitoring the number of unique values and missing values is crucial for detecting drift.

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Fletcher

1 year 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

1 year 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

1 year 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

1 year 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

1 year 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

1 year ago

But monitoring the percentage of missing values is also important to detect drift in categorical features.

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My

1 year ago

I disagree, I believe the answer is E) Number of unique values.

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Nieves

1 year ago

I think the answer is C) Mode, number of unique values, and percentage of missing values.

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