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Question 103/137
You need to create a working environment in Vertex AI Workbench for a team of data scientists. Each data scientist has different VM CPU and RAM and package requirements, and will be assigned a personal notebook instance. You want each instance to have a custom set of packages pre-installed. Your company wants to minimize the running cost of notebook instances. How should you create the environment?
Correct Answer: C
This scenario requires balancing customization with cost-efficiency using Vertex AI Workbench.
* Custom Container Images: Since the team requires specific, pre-installed packages that are not part of the standard Google Deep Learning Containers (DLC), creating a custom image derived from a DLC is the most efficient way to ensure environment consistency across instances.
* Cost Management: To minimize costs, " Idle Shutdown " is a critical feature. It automatically stops the notebook instance when it is not in use, ensuring you only pay for compute time actually utilized.
* Why Option C is better than D: The prompt explicitly states that each data scientist has different VM CPU and RAM requirements. Setting a fixed " default " machine type like n1-standard-4 (Option D) contradicts the requirement to cater to different hardware needs and could lead to over-provisioning (wasted cost) or under-provisioning for specific users. Option C focuses on the custom image and the cost-saving mechanism (Idle Shutdown) while allowing for hardware flexibility.
* Custom Container Images: Since the team requires specific, pre-installed packages that are not part of the standard Google Deep Learning Containers (DLC), creating a custom image derived from a DLC is the most efficient way to ensure environment consistency across instances.
* Cost Management: To minimize costs, " Idle Shutdown " is a critical feature. It automatically stops the notebook instance when it is not in use, ensuring you only pay for compute time actually utilized.
* Why Option C is better than D: The prompt explicitly states that each data scientist has different VM CPU and RAM requirements. Setting a fixed " default " machine type like n1-standard-4 (Option D) contradicts the requirement to cater to different hardware needs and could lead to over-provisioning (wasted cost) or under-provisioning for specific users. Option C focuses on the custom image and the cost-saving mechanism (Idle Shutdown) while allowing for hardware flexibility.
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