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PDE Practice Question: A data science team needs to ensure that a…
A data science team needs to ensure that a deployed Vertex AI model can handle varying traffic patterns with minimal latency and cost. What should they do?
⚠ Common exam trap
Google Cloud often tests the misconception that batch prediction can substitute for online serving in variable traffic scenarios, but the key distinction is that batch prediction lacks real-time latency guarantees and cannot scale dynamically per request.
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
✓
Use Vertex AI Prediction with autoscaling
Vertex AI Prediction with autoscaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring minimal latency during spikes and cost efficiency during lulls. This is the recommended approach for handling variable traffic patterns in production, as it leverages Google Cloud's managed infrastructure to scale from zero to thousands of nodes automatically.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Vertex AI Prediction with autoscaling
Why this is correct
Autoscaling adjusts replicas based on traffic, balancing latency and cost.
- ✗
Use batch prediction instead of online
Why it's wrong here
Batch prediction is not designed for real-time low-latency predictions.
- ✗
Pre-warm all instances
Why it's wrong here
Pre-warming all instances is costly and does not handle traffic variance efficiently.
- ✗
Deploy to a single large machine type
Why it's wrong here
Single large machine may underutilize resources and does not scale dynamically.
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