Professional Machine Learning Engineer Kubeflow Practice Questions

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

• AA. Use the Natural Language API to classify support requests
• BB. Use AutoML Natural Language to build the support requests classifier
• CC. Use an established text classification model on Al Platform to perform transfer learning
• DD. Use an established text classification model on Al Platform as-is to classify support requests

You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

• AA. Significantly increase the max_batch_size TensorFlow Serving parameter
• BB. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
• CC. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
• DD. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes