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Databricks Certified Professional Data Scientist Exam - Topic 3 Question 89 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 89
Topic #: 3
[All Databricks Certified Professional Data Scientist Questions]

Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

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Suggested Answer: C

by Nenita at Nov 24, 2025, 05:35 PM

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Howard
12 hours ago
Cluster density isn't relevant for recommender systems, good to know.
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Audra
6 days ago
Wait, support vector count doesn't apply? That's surprising!
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Moira
11 days ago
I disagree, sum of absolute errors is still useful in some contexts.
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Gertude
16 days ago
MAE and AUC are both good choices, but I'd go with MAE since it's more intuitive to understand.
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Lindy
21 days ago
Cluster density? Sounds more like a question about data clustering than recommender systems.
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Eliseo
26 days ago
I agree, the MAE is a straightforward way to quantify the average error in the recommendations.
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Kati
1 month ago
C) Mean Absolute Error seems like the most relevant metric to measure the accuracy of a recommender system.
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Quentin
1 month ago
I definitely remember that MAE is useful, but I’m a bit confused about when to use the sum of absolute errors. Is it really that helpful for assessing performance?
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Stefania
1 month ago
Cluster density and support vector count sound familiar, but I can't recall how they relate to recommender systems. I feel like they might be more relevant to clustering or classification tasks.
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Karan
2 months ago
I think the sum of absolute errors is mentioned in some practice questions, but it seems less informative than MAE for recommender systems.
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Annmarie
2 months ago
I remember studying that MAE is a key metric for evaluating recommender systems, but I'm not entirely sure how it compares to other metrics like AUC.
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Tasia
2 months ago
Okay, got it. MAE measures the average error, which is a good indicator of a recommender system's accuracy. I think I can craft a solid response explaining why that's a useful metric.
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Soledad
2 months ago
MAE is definitely a key metric for accuracy!
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Carma
2 months ago
Hmm, I'm not too familiar with recommender system metrics. I'll need to review the library references on MAE to make sure I understand it fully before answering this.
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Novella
2 months ago
The question seems straightforward - MAE and AUC are the key metrics mentioned, so I'd focus on explaining those in my answer. The other options don't seem relevant.
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Henriette
3 months ago
I'm a bit confused on the difference between MAE and the sum of absolute errors. Can someone clarify which one is the better metric for recommender systems?
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Shenika
3 months ago
I think the MAE and AUC metrics would be the most useful for evaluating a recommender system. The MAE gives a good sense of the average error, while AUC measures the overall accuracy.
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Edwin
3 months ago
But what about the sum of absolute errors?
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Billye
3 months ago
AUC is great for overall accuracy too!
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