Q: 6
In your AI data center, you are responsible for deploying and managing multiple machine learning
models in production. To streamline this process, you decide to implement MLOps practices with a
focus on job scheduling and orchestration. Which of the following strategies is most aligned with
achieving reliable and efficient model deployment?
Options
Discussion
Option A fits best. Automating with a CI/CD pipeline lines up with official MLOps practices and NVIDIA recommendations for job scheduling and deployment. Saw similar advice in the official guide and practice tests, so pretty sure this is right.
Option A looks right. CI/CD automation is what gets you reliable and efficient deployment in MLOps. Manual steps or skipping staging (C, D) usually introduce risk or delays. I've seen this called out in both NVIDIA docs and real-world setups. Pretty confident here but tell me if you see it differently.
Probably A
A
Seen similar in official guide and practice exams, it's A.
A
This vendor loves to throw in those manual deployment traps just to confuse people. A
C or D
A or maybe B, but official guide and hands-on labs agree with A for real MLOps practice.
Feels like A makes the most sense here. CI/CD automation streamlines everything in MLOps so you aren't relying on manual steps or skipping validation. Manual deployments (C) and skipping staging (D) miss the reliability part. Pretty sure A is right but open to other ideas.
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