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PMLE Practice Question: A company deploys a TensorFlow model on Vertex AI…
A company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?
⚠ Common exam trap
Candidates often confuse improving throughput (batching or bigger machines) with reducing latency under load, but the first action should always be to add more replicas via autoscaling to handle concurrent requests, not to optimize a single node's performance.
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
✓
Enable autoscaling for the deployment
Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable autoscaling for the deployment
Why this is correct
Autoscaling adds nodes during peak traffic, reducing latency.
- ✗
Increase the machine type of the node
Why it's wrong here
Increasing machine type may help but does not address scaling under load.
- ✗
Decrease the min replicas to 0
Why it's wrong here
Reducing min replicas may cause cold starts and increase latency.
- ✗
Enable automatic batching of requests
Why it's wrong here
Batching increases latency as requests wait to be batched.
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Senior Network & Security Engineer · founder of Courseiva
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