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PDE Practice Question: A startup is deploying a PyTorch model on Google…
A startup is deploying a PyTorch model on Google Cloud. They need to serve predictions for a mobile app with bursty traffic. Which service is most cost-effective?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Endpoints with autoscaling and a minimum of 0 replicas to scale down to zero
Most cost-effective because Vertex AI Endpoints with autoscaling and minimum of 0 replicas can scale down to zero when idle, eliminating cost during no traffic. This is ideal for bursty traffic patterns. Option B has minimum replicas, incurring cost even when idle. Option C (App Engine manual scaling) does not scale to zero easily. Option D (Cloud Run with CPU always allocated) charges for CPU even when idle, making it less cost-effective for bursty traffic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Endpoints with autoscaling and a minimum of 0 replicas to scale down to zero
Why this is correct
Scaling to zero minimizes cost when idle, ideal for bursty traffic.
- ✗
Vertex AI Endpoints with a minimum number of replicas
Why it's wrong here
Minimum replicas ensure uptime but cost when idle.
- ✗
App Engine with manual scaling
Why it's wrong here
Manual scaling requires operator intervention, not cost-effective for bursty traffic.
- ✗
Cloud Run with CPU always allocated
Why it's wrong here
CPU always allocated incurs costs, and Cloud Run is not optimized for ML model serving.
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This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.