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Google Exam Professional-Machine-Learning-Engineer Topic 3 Question 91 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam
Question #: 91
Topic #: 3
[All Google Professional Machine Learning Engineer Questions]

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user's recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh dat

a. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

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Suggested Answer: C

Traffic splitting is a feature of Vertex AI that allows you to distribute the prediction requests among multiple models or model versions within the same endpoint. You can specify the percentage of traffic that each model or model version receives, and change it at any time. Traffic splitting can help you test the new model in production without creating a new endpoint or a separate service. You can deploy the new model to the existing Vertex AI endpoint, and use traffic splitting to send 5% of production traffic to the new model. You can monitor the end-user metrics, such as listening time, to compare the performance of the new model and the previous model. If the end-user metrics improve between models over time, you can gradually increase the percentage of production traffic sent to the new model. This solution can help you test the new model in production while minimizing complexity and cost.Reference:

Traffic splitting | Vertex AI

Deploying models to endpoints | Vertex AI


Contribute your Thoughts:

Daren
19 hours ago
A reminds me of A/B testing, which is a classic approach. I wonder if the random 5% split might lead to some users getting a suboptimal experience though.
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Vincenza
3 days ago
D is a clever idea, using monitoring to automatically update the model. But I'm not sure I'd trust the drift detection to work perfectly on the first try.
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Miesha
4 days ago
I agree with Lorrie. Option A seems like the most efficient way to test the new model while minimizing complexity.
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Lorrie
9 days ago
I think option A is the best choice. It allows for gradual testing of the new model in production.
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Ling
15 days ago
B looks interesting, but capturing all the prediction requests in BigQuery could get costly. I'd prefer a more targeted approach like C.
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Sheldon
16 days ago
Option C seems the most straightforward and minimizes disruption to the production environment. I like how it gradually ramps up the new model's traffic to monitor performance.
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Latricia
5 days ago
Option C seems like a good choice. It allows for gradual testing of the new model.
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Deangelo
13 days ago
Option C seems like the best approach. It allows for gradual testing of the new model without causing too much disruption.
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