Exam Professional Machine Learning Engineer topic 1 question 256 discussion

Option A: Manually adding compute nodes after an alert might lead to delays and potential outages during peak traffic. Option B: Upgrading to 32 vCPUs upfront might be an overkill if the current machine type with 8 vCPUs can handle the typical daily traffic. Vertical scaling (more vCPUs) might be suitable only if the model can benefit from additional CPU power. Option D: Using a GPU is unlikely to benefit a recipe recommendation model, which likely doesn't involve intensive graphical processing. Additionally, monitoring GPU usage wouldn't be relevant.

C) Option C is the best because: 1) It leverages the built-in autoscaling capabilities of Vertex AI. 2) It's the most efficient/cost-effective solution for fluctuating traffic. 2) Manually scaling (options A and B) is reactive and inefficient 3) A GPU is unnecessary, there is no intensive graphical processing

C is right because 1)Since you've already optimized your model's deployment on a single machine with 8 vCPUs, it makes sense to maintain the same machine type to avoid any potential performance issues. 2)Enabling autoscaling based on vCPU usage will allow your endpoint to automatically add more machines as needed to handle the increased traffic during the holiday season. This approach is more efficient and cost-effective than scaling up individual machines or adding new machines manually. 3)Monitoring CPU usage with a job and alerting when thresholds are exceeded allows you to detect potential issues before they impact performance.

C: Use Autoscaling Based on vCPU Usage

Autoscaling based on vCPU usage aligns well with the workload.

Option A is manual intervention Option B is overprovisioning preemptively, which is an overkill ( autoscaling should be preferred) Option D - Unless the recipe recommendation model uses GPU-accelerated computations (e.g., some deep learning models), adding a GPU won't be beneficial and will increase costs. I would go with C - Autoscaling based on vCPU usage which aligns well with the workload.

Option B can only support exact 4x times traffic, but the requirement is four times "more", so B is not the best at least for me.

I would go for C as it enables autoscaling when exceeding a determined CPU usage threshold.

Cost Optimization: It starts with the current machine type, avoiding unnecessary upfront costs, and scales only when needed. Autoscaling: It automatically adjusts compute resources based on vCPU usage, ensuring the endpoint can handle traffic spikes without manual intervention. Monitoring and Alerting: It provides visibility into resource usage and triggers alerts for potential issues, enabling proactive actions. Investigation: It encourages investigation of alerts to identify any underlying problems beyond expected traffic growth, ensuring overall system health.

Voting for B as it's the only option to autoscale even though the cost will go up. All other options include manual intervention.