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Question 160 - Professional Machine Learning Engineer discussion

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You recently deployed a model to a Vertex Al endpoint Your data drifts frequently so you have enabled request-response logging and created a Vertex Al Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift. What should you do?

A.
Replace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV).
Answers
A.
Replace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV).
B.
Replace the monitoring job with a custom SQL scnpt to calculate statistics on the features and predictions in BigQuery.
Answers
B.
Replace the monitoring job with a custom SQL scnpt to calculate statistics on the features and predictions in BigQuery.
C.
Decrease the sample_rate parameter in the Randomsampleconfig of the monitoring job.
Answers
C.
Decrease the sample_rate parameter in the Randomsampleconfig of the monitoring job.
D.
Increase the monitor_interval parameter in the scheduieconfig of the monitoring job.
Answers
D.
Increase the monitor_interval parameter in the scheduieconfig of the monitoring job.
Suggested answer: C

Explanation:

According to the official exam guide1, one of the skills assessed in the exam is to ''configure and optimize model monitoring jobs''. The Vertex AI Model Monitoring documentation states that ''to reduce the cost of model monitoring, you can configure the sample rate of the requests that are logged and analyzed by model monitoring''. Therefore, decreasing the sample_rate parameter in the Randomsampleconfig of the monitoring job would reduce the model monitoring cost while continuing to quickly detect drift. The other options are not relevant or optimal for this scenario.Reference:

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[Vertex AI Model Monitoring]

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asked 18/09/2024
Shaharyar Chaudhry
42 questions
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