Google Cloud Professional Machine Learning Engineer — Question 268

Your work for a textile manufacturing company. Your company has hundreds of machines, and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies. Models are retrained daily, and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

Answer options

• A. Deploy a Dataflow batch pipeline and a Vertex AI Prediction endpoint.
• B. Deploy a Dataflow batch pipeline with the Runlnference API, and use model refresh.
• C. Deploy a Dataflow streaming pipeline and a Vertex AI Prediction endpoint with autoscaling.
• D. Deploy a Dataflow streaming pipeline with the Runlnference API, and use automatic model refresh.

Correct answer: D

Explanation

The correct answer is D because deploying a Dataflow streaming pipeline with the Runlnference API allows for real-time inference and automatic model refresh, ensuring that the models are always up-to-date and can provide predictions with minimal latency. Options A and B focus on batch processing, which is not suitable for 24/7 operation without downtime. Option C, while it mentions autoscaling, still lacks the automatic model refresh aspect that is critical for adapting to new data continuously.