Scaling Vertex AI Endpoints to Fix HTTP 429 Errors
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Refer to the exhibit. A Vertex AI endpoint configured with the above deployment is returning HTTP 429 (Too Many Requests) errors during peak traffic. The current CPU utilization reaches 80% consistently. What should the team adjust to resolve this?
The correct adjustment is to increase maxReplicaCount to 10. HTTP 429 errors on a Vertex AI endpoint signal that the deployment lacks sufficient capacity to handle incoming requests, and raising maxReplicaCount allows the autoscaler to spin up more replicas—up to ten—when CPU utilization hits the configured target, directly resolving the shortage. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of Vertex AI endpoint scaling behavior: the trap is to confuse machine type changes or scale target adjustments with capacity, but only increasing the maximum replica count expands the pool of available instances under load. A reliable memory tip is “429 = 4 replicas too few; maxReplicaCount is your ceiling,” reminding you that errors stem from hitting a cap, not from underpowered hardware.
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
✓
Increase maxReplicaCount to 10
Increasing maxReplicaCount to 10 allows the Vertex AI endpoint to scale out to more instances during peak traffic, distributing the load and reducing HTTP 429 errors. Since CPU utilization is at 80%, the current maxReplicaCount is insufficient to handle the demand, and raising this limit enables the horizontal pod autoscaler to add replicas up to the new maximum, directly addressing the capacity bottleneck.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Increase maxReplicaCount to 10
Why this is correct
Correct: Higher max allows more replicas to handle traffic spikes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase scaleTarget to 0.9
Why it's wrong here
Higher target delays scaling, worsening the issue.
✗
Change machineType to n1-highmem-2
Why it's wrong here
Memory improvement does not address CPU-bound scaling.
✗
Increase minReplicaCount to 2
Why it's wrong here
This ensures at least 2 replicas but still limits maximum capacity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google Cloud Gen AI Leader exam often tests the distinction between scaling limits (min/maxReplicaCount) and scaling thresholds (scaleTarget), trapping candidates who confuse raising the scaling target with increasing capacity.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use horizontal pod autoscaling (HPA) based on target CPU utilization (default 0.6, or 60%). When CPU exceeds the target, the HPA calculates desired replicas as ceil(currentLoad / targetUtilization). With maxReplicaCount capped, the autoscaler cannot provision enough replicas to bring CPU below the target, causing sustained high utilization and eventual 429 errors as the endpoint's request queue overflows. In production, setting maxReplicaCount too low is a common misconfiguration that leads to throttling even when the cluster has spare capacity.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Visual reference
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase maxReplicaCount to 10 — Increasing maxReplicaCount to 10 allows the Vertex AI endpoint to scale out to more instances during peak traffic, distributing the load and reducing HTTP 429 errors. Since CPU utilization is at 80%, the current maxReplicaCount is insufficient to handle the demand, and raising this limit enables the horizontal pod autoscaler to add replicas up to the new maximum, directly addressing the capacity bottleneck.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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