Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
The data scientists at your company have built a machine learning neural network model using TensorFlow. After several tests on the model, the team decides the model is ready to be deployed for production use. Which of the following services would you use to host the model to Google Cloud?
A. Google Kubernetes Engine
B. Google ML Deep Learning VM
C. Google Container Registry
D. Vertex AI Machine Learning Model
Answer: D
Google Kubernetes Engine is a managed, production-ready environment for deploying containerized applications. It brings our latest innovations in developer productivity, resource efficiency, automated operations, and open-source flexibility to accelerate your time to market.
Option A is incorrect: GKE is a service to deploy and scale Docker containers in the cloud. You need to build the docker image for your model if you want to use it, which is not recommended for this scenario.
Option B is incorrect: Google ML Deep Learning VM is a service that offers pre-configured virtual machines for deep learning applications. It is not used to deploy ML models to production.
Option C is incorrect: Google Container Registry is a service to store, manage, and secure your Docker container images. It does not for deploying machine learning models.
Cloud Machine Learning Engine is a managed service that lets developers and data scientists build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually.
Option D is correct: Google Vertex AI Auto ML is the service to use to deploy your machine learning models.
Source(s):
Vertex AI: https://cloud.google.com/vertex-ai
Google Machine Learning Engine: https://cloud.google.com/ml-engine/
Google ML Deep Learning VM: https://cloud.google.com/deep-learning-vm/
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