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Databricks Exam Databricks-Machine-Learning-Associate Topic 3 Question 25 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 25
Topic #: 3
[All Databricks-Machine-Learning-Associate Questions]

A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.

In which situation will the machine learning engineer be correct?

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Suggested Answer: D

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.


Model Ensemble Techniques

Contribute your Thoughts:

Virgina
2 months ago
You know, I think the correct answer is when the new solution requires each model to compute a prediction for every record. That's got to be less efficient.
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Florencia
2 months ago
Haha, I bet the engineer just wants to keep using the old model. Gotta love that resistance to change!
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Titus
20 days ago
D) When the new solution requires that each model computes a prediction for every record
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Santos
22 days ago
C) When the new solution requires the use of fewer feature variables than the original model
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Gregg
23 days ago
B) When the new solution's models have an average latency that is larger than the size of the original model
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Walker
27 days ago
A) When the new solution requires if-else logic determining which model to use to compute each prediction
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Ronny
2 months ago
Wait, what if the new models are just bigger in size? That could definitely slow things down during inference.
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Hollis
29 days ago
B) When the new solution's models have an average size that is larger than the size of the original model
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Alyssa
1 months ago
A) When the new solution requires if-else logic determining which model to use to compute each prediction
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German
2 months ago
But what if the new solution's models have an average size larger than the original model? Wouldn't that also make it less time efficient?
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Tracey
2 months ago
Nah, I don't think that's the case. If the latency is the same, the extra logic shouldn't make a difference.
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Aliza
1 months ago
Nah, I don't think that's the case. If the latency is the same, the extra logic shouldn't make a difference.
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Louisa
2 months ago
A) When the new solution requires if-else logic determining which model to use to compute each prediction
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Junita
2 months ago
Hmm, I think the engineer is right. If the new solution requires if-else logic, that's gonna add some overhead, right?
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Sage
1 months ago
D) When the new solution requires that each model computes a prediction for every record
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My
2 months ago
C) When the new solution requires the use of fewer feature variables than the original model
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Daron
2 months ago
B) When the new solution's models have an average latency that is larger than the size of the original model
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Louvenia
2 months ago
A) When the new solution requires if-else logic determining which model to use to compute each prediction
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Floyd
2 months ago
I agree with Stefany. If there's if-else logic, it will definitely slow down the inference process.
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Stefany
2 months ago
I think the machine learning engineer will be correct when the new solution requires if-else logic to determine which model to use.
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