PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #162
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #162: Real Exam Question with Answer & Explanation
Sign in or unlock PROFESSIONAL-MACHINE-LEARNING-ENGINEER to reveal the answer and full explanation for question #162. The question stem and answer options stay visible for context.
Question
While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?
Options
- AInclude the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on
- BMove the evaluation step out of your pipeline and run it on custom Compute Engine VMs with
- CMigrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the
- DAdd tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
Unlock PROFESSIONAL-MACHINE-LEARNING-ENGINEER to see the answer
You've previewed enough free PROFESSIONAL-MACHINE-LEARNING-ENGINEER questions. Unlock PROFESSIONAL-MACHINE-LEARNING-ENGINEER for full answers, explanations, the timed quiz mode, progress tracking, and the master PDF. Question stem and options stay visible so you can still see what's on the exam.