- A
Use a larger machine type (e.g., n1-highmem-8) instead of the current n1-standard-4 to improve per-replica throughput.
Why wrong: While larger machines might help, they can be cost-inefficient; autoscaling and global endpoint are more effective.
- B
Enable autoscaling with a higher max replica count and configure a CPU utilization target of 60%.
Increasing max replicas and tuning CPU utilization target helps handle peak load and reduce latency.
- C
Reduce the min replica count to 0 to allow the service to scale down to zero when not in use.
Why wrong: Scaling to zero would cause cold start latency and cannot handle sudden traffic spikes.
- D
Deploy the model as a batch prediction job and move all online predictions to batch.
Why wrong: Batch prediction is not suitable for real-time online serving, which is required here.
- E
Switch to a global endpoint with automatic scaling to distribute traffic across multiple regions.
Global endpoints can reduce latency for geographically distributed users and provide better load distribution.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A data science team has deployed a custom TensorFlow model on Vertex AI Prediction. They notice increasing prediction latency and a growing number of 503 errors during peak traffic hours. The model is served using a single regional endpoint with min replica count of 2 and max replica count of 10. Which TWO actions should the team take to address these issues?
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 with a higher max replica count and configure a CPU utilization target of 60%.
Option B is correct because increasing the max replica count and setting a CPU utilization target of 60% allows the Vertex AI Prediction service to scale out more aggressively during traffic spikes, reducing both latency and 503 errors. This directly addresses the bottleneck of insufficient compute capacity under peak load, as the current max of 10 replicas may be too low to handle demand.
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.
- ✗
Use a larger machine type (e.g., n1-highmem-8) instead of the current n1-standard-4 to improve per-replica throughput.
Why it's wrong here
While larger machines might help, they can be cost-inefficient; autoscaling and global endpoint are more effective.
- ✓
Enable autoscaling with a higher max replica count and configure a CPU utilization target of 60%.
Why this is correct
Increasing max replicas and tuning CPU utilization target helps handle peak load and reduce latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the min replica count to 0 to allow the service to scale down to zero when not in use.
Why it's wrong here
Scaling to zero would cause cold start latency and cannot handle sudden traffic spikes.
- ✗
Deploy the model as a batch prediction job and move all online predictions to batch.
Why it's wrong here
Batch prediction is not suitable for real-time online serving, which is required here.
- ✓
Switch to a global endpoint with automatic scaling to distribute traffic across multiple regions.
Why this is correct
Global endpoints can reduce latency for geographically distributed users and provide better load distribution.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume upgrading machine types (Option A) is the primary fix for latency, when in reality the issue is insufficient scaling capacity under peak load, not per-replica performance.
Detailed technical explanation
How to think about this question
Vertex AI Prediction uses horizontal pod autoscaling based on CPU utilization or custom metrics; a target of 60% ensures replicas are added before CPU saturation causes queuing and timeouts. The 503 errors typically indicate that the prediction service is overwhelmed and returning HTTP 503 (Service Unavailable) when all replicas are busy and the autoscaler cannot add more due to the max replica cap. Global endpoints (Option E) distribute traffic across multiple regions, reducing latency by routing users to the nearest healthy region and providing regional failover, which is especially effective for global user bases.
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable autoscaling with a higher max replica count and configure a CPU utilization target of 60%. — Option B is correct because increasing the max replica count and setting a CPU utilization target of 60% allows the Vertex AI Prediction service to scale out more aggressively during traffic spikes, reducing both latency and 503 errors. This directly addresses the bottleneck of insufficient compute capacity under peak load, as the current max of 10 replicas may be too low to handle demand.
What should I do if I get this PDE 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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Last reviewed: Jul 4, 2026
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