Question 32 of 280
Single answerYou developed an ML model with AI 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?
✓ASignificantly increase the max_batch_size TensorFlow Serving parameter.
BSwitch to the tensorflow-model-server-universal version of TensorFlow Serving.
CSignificantly increase the max_enqueued_batches TensorFlow Serving parameter.
DRecompile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to
choose an appropriate baseline minimum CPU platform for serving nodes.
✓
Correct Answer: A
Significantly increase the max_batch_size TensorFlow Serving parameter.
▥
Explanation
The correct answer is highlighted above. Review the wording carefully, then use the next question to continue building your understanding of Google certification topics.