Google Exam Professional Cloud Developer Topic 4 Question 77 Discussion

Actual exam question for Google's Professional Cloud Developer exam

Question #: 77
Topic #: 4

[All Professional Cloud Developer Questions]

You have an application running in a production Google Kubernetes Engine (GKE) cluster. You use Cloud Deploy to automatically deploy your application to your production GKE cluster. As part of your development process: you are planning to make frequent changes to the applications source code and need to select the tools to test the changes before pushing them to your remote source code repository. Your toolset must meet the following requirements:

* Test frequent local changes automatically.

* Local deployment emulates production deployment.

Which tools should you use to test building and running a container on your laptop using minimal resources'?

ATerraform and kubeadm

BDocker Compose and dockerd

CMinikube and Skaffold

Dkaniko and Tekton

Show Suggested Answer

Suggested Answer: B

by Fletcher at Mar 06, 2024, 09:42 AM

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Jaime

7 months ago

D) kaniko and Tekton are great too, but they might be a bit more complex to set up compared to Minikube and Skaffold.

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Kenneth

7 months ago

What about D) kaniko and Tekton? They are also good tools for building and testing containers locally.

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Izetta

7 months ago

I agree with C), Minikube allows for local deployment that emulates the production environment.

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Jaime

7 months ago

I think C) Minikube and Skaffold would be a good choice for testing the changes locally.

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Caprice

8 months ago

Haha, Mitsue, you make a good point. It's like the age-old debate - 'will it work on my machine?'. At least with Minikube and Skaffold, we'd be closer to the real thing.

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Mitsue

8 months ago

Ah yes, the old 'Docker Compose and dockerd' trick. Works like a charm! Although, I do wonder if it really emulates the production environment well enough.

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Mary

8 months ago

That's a good point, Mei. Maybe Docker Compose and dockerd would be a simpler solution. We can test the container locally without the overhead of a full Kubernetes cluster.

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