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Exam Professional Machine Learning Engineer All Questions

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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
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Suggested Answer: A 🗳️
by pikachu007 at Jan. 13, 2024, 3:15 a.m.

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b1a8fae
Highly Voted 9 months, 2 weeks 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 6 months, 2 weeks 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
8 months, 2 weeks 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
5 months, 3 weeks 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
9 months, 2 weeks 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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