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You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take

Sagot :

The answer is  option C.

Perform the following:

1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.

2) Build a Docker image from the production branch with all of the dependencies, and tag it with the

3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.

What is Python used for?

Python is known to be a form of computer programming language that is known to be used so as to create websites and software, automate tasks, and others.

Note that Python is said to be a general-purpose language and as such, for the case above, The answer is  option C.

Perform the following:

1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.

2) Build a Docker image from the production branch with all of the dependencies, and tag it with the

3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.

See options below

A.

Perform the following:

1) Create a managed instance group with f1-micro type machines.

2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.

3) Restart the instances to automatically deploy new production releases.

B.

Perform the following:

1) Create a managed instance group with n1-standard-1 type machines.

2) Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.

3) Rebuild the Compute Engine image, and update the instance template to deploy new production releases.

C.

Perform the following:

1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.

2) Build a Docker image from the production branch with all of the dependencies, and tag it with the

3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.

D.

Perform the following:

1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.

2) Build a Docker image from the master branch will all of the dependencies, and tag it with "latest".

3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to "Always".

Restart the pods to automatically deploy new production releases.

Learn more about Python from

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