Professional-Machine-Learning-Engineer Exam Dumps | Your work for a textile manufacturing company. Your company has hundreds of machines and each machine

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Question 101/139

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?

A. Deploy a Dataflow batch pipeline and a Vertex Al 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 Al Prediction endpoint with autoscaling.

D. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

Correct Answer: D

A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to- date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. References:
* Dataflow documentation
* RunInference API documentation
* Automatic model refresh documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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