Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are building a machine learning model using TensorFlow. The model will read images of nature to detect the weather (sunny, cloudy, ..), season and other predictions. Your model is trained and ready to be deployed for use in the production environment. You decide to deploy it in Google Cloud. Which of the following products should you use?
A. Google ML Deep Learning VM
B. Google Container Registry
C. Google Kubernetes Engine
D. Vertex AI
Correct Answer: D
Vertex AI brings AutoML and AI Platform together into a unified API, client library, and user interface. AutoML lets you, train models, on the image, tabular, text, and video datasets without writing code, while training in AI Platform lets you run custom training code. With Vertex AI, both AutoML training and custom training are available options.
Option A 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 B is incorrect: Google Container Registry is a service to store, manage, and secure your Docker container images. It is not used for deploying machine learning models.
Option C is incorrect: GKE is a service to deploy and scale docker containers in the cloud. You will have to build the docker image for your model if you want to use it, which is not recommended for this scenario.
Source(s):
- Google Kubernetes Engine: https://cloud.google.com/kubernetes-engine/
- Google Machine Learning Engine: https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform
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