Exam Professional Machine Learning Engineer topic 1 question 188 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 188
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You work at a bank. You have a custom tabular ML model that was provided by the bank’s vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex AI Model serving container, which accepts a string as input for each prediction instance. In each string, the feature values are separated by commas. You want to deploy this model to production for online predictions and monitor the feature distribution over time with minimal effort. What should you do?

A. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
2. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema
B. 1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
2. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and provide an instance schema
C. 1. Refactor the serving container to accept key-value pairs as input format
2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
3. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective.
D. 1. Refactor the serving container to accept key-value pairs as input format
2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint
3. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective

Show Suggested Answer

Suggested Answer: A 🗳️

by pikachu007 at Jan. 13, 2024, 3:15 a.m.

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b1a8fae

Highly Voted 1 year ago

Selected Answer: A

A. Minimum effort -> ditch refactoring (hopefully not needed) Training data not available -> can't be skew, so it must be drift

upvoted 5 times

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pinimichele01

Most Recent 10 months ago

Selected Answer: A

Training data not available -> can't be skew, so it must be drift

upvoted 3 times

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CHARLIE2108

12 months ago

I have a doubt, could someone please help with this? While "drift" (Option A) might imply gradual changes, "skew" (Option B) is more suitable for sudden shifts in feature distributions, potentially relevant for sensitive data. Is option B better than A?

upvoted 1 times

tavva_prudhvi

9 months, 1 week ago

Feature skew is typically used to compare the feature distribution between training data and serving data, which is not as relevant here because you do not have access to the training data. Therefore, Option B is less suitable.

upvoted 4 times

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pikachu007

1 year ago

Selected Answer: A

Handles string input format: Vertex AI Model Monitoring can parse comma-separated feature values, avoiding the need to refactor the serving container. It directly monitors feature distribution over time, aligning with the goal of detecting potential drifts.

upvoted 1 times

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