Title: Quiz 15 — Automated Model Deployment, Monitoring & Cost Optimization
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 15 — Automated Model Deployment, Monitoring & Cost Optimization

Q: In a blue-green deployment, what happens once the new ("Green") environment is validated?
- Traffic is gradually shifted to Green over several hours
- All traffic is cut over from Blue to Green at once
- Blue and Green permanently split traffic 50/50
- Green replaces Blue's code without a new environment being created
A: All traffic is cut over from Blue to Green at once

Q: Canary deployments can be implemented with just a percentage value in a configuration file, without any additional infrastructure.
- True
- False
A: False

Q: In the full automated pipeline from lecture, which stage comes immediately before "CD deploy"?
- Production monitoring
- Containerize
- Code commit
- Auto-rollback
A: Containerize

Q: Why is a deployment pipeline considered incomplete if it lacks a tested rollback plan?
Feedback: Sample/expected answer: Because when a new version misbehaves in production, the team needs to be able to quickly revert to the last known-good version; an untested rollback plan may not actually work when it's needed, leaving a bad release live longer than necessary.

Q: Which CloudWatch concept represents numerical time-series data, such as latency sampled every minute?
- Logs
- Alarms
- Metrics
- Anomaly bands
A: Metrics

Q: A team wants to reduce cloud costs and is deciding what to do first. Which sequence follows the guidance from this week's lecture?
- Buy Reserved Instances first, then rightsize afterward
- Rightsize based on actual utilization first, then commit to Reserved Instances/Savings Plans
- Schedule off all instances immediately regardless of usage
- Skip rightsizing since Savings Plans already guarantee the best price
A: Rightsize based on actual utilization first, then commit to Reserved Instances/Savings Plans

Q: More automation in a deployment pipeline always indicates a more mature MLOps practice.
- True
- False
A: False

Q: Roughly what GPU utilization rate is typical across production cloud AI deployments, and what does that imply about idle billed capacity?
Feedback: Sample/expected answer: Typically only 30–50% utilized, meaning over half of expensive GPU capacity often sits idle while still being billed.