- A
Increase the timeout for predictions on the NN endpoint to avoid fallback.
Why wrong: Increasing timeout may not solve the underlying latency issue and could lead to slower user experience.
- B
Enable fallback logic to use the GBT model when NN times out, ensuring no prediction is missed.
Why wrong: Fallback is already happening causing accuracy drop; better to remove the faulty model from serving.
- C
Temporarily reduce the traffic percentage to the NN model to 0% and redistribute to GBT and LR until the NN issue is resolved.
This immediately stops the problematic model from serving and restores accuracy using the other models.
- D
Relaunch the NN model with a larger machine type and more replicas to reduce latency.
Why wrong: This takes time and may not resolve the root cause; priority is to restore accuracy quickly.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your team manages a multi-model ensemble deployed on Vertex AI Endpoint. The ensemble consists of three models: a neural network (NN), a gradient boosted tree (GBT), and a logistic regression (LR). They are deployed as separate endpoints and traffic is split using a traffic split configuration. Recently, the overall accuracy dropped from 92% to 85%. Monitoring shows that the NN model's latency has increased significantly, causing it to miss timeouts and fall back to default predictions. The other two models are performing normally. The NN model is the most complex and handles the majority of the traffic. You need to restore accuracy quickly. What should you do first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Temporarily reduce the traffic percentage to the NN model to 0% and redistribute to GBT and LR until the NN issue is resolved.
Option C is correct because the immediate priority is to stop routing traffic to the failing NN model, which is causing timeouts and fallback to default predictions, thereby restoring accuracy quickly. By setting the NN endpoint's traffic percentage to 0% and redistributing to the healthy GBT and LR models, you eliminate the source of degraded predictions without requiring a redeployment or configuration change that could take time. This leverages Vertex AI's traffic split capability to isolate the faulty model while you diagnose and fix the latency issue.
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 the timeout for predictions on the NN endpoint to avoid fallback.
Why it's wrong here
Increasing timeout may not solve the underlying latency issue and could lead to slower user experience.
- ✗
Enable fallback logic to use the GBT model when NN times out, ensuring no prediction is missed.
Why it's wrong here
Fallback is already happening causing accuracy drop; better to remove the faulty model from serving.
- ✓
Temporarily reduce the traffic percentage to the NN model to 0% and redistribute to GBT and LR until the NN issue is resolved.
Why this is correct
This immediately stops the problematic model from serving and restores accuracy using the other models.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Relaunch the NN model with a larger machine type and more replicas to reduce latency.
Why it's wrong here
This takes time and may not resolve the root cause; priority is to restore accuracy quickly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think increasing timeout or adding fallback logic (options A or B) will fix the accuracy issue, but they fail to recognize that the NN is still receiving traffic and producing degraded predictions, whereas the correct first step is to stop routing traffic to the failing model entirely.
Detailed technical explanation
How to think about this question
Vertex AI Endpoint traffic splits are implemented via the `traffic_split` parameter in the endpoint configuration, which maps model versions to percentage weights. When a model times out, Vertex AI returns a default prediction (often a static value or a pre-configured fallback), which can drastically skew ensemble accuracy. In practice, the NN model's increased latency might be due to resource contention or a memory leak, and temporarily zeroing its traffic allows you to perform A/B testing or rollback without disrupting the serving infrastructure.
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
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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.
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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: Temporarily reduce the traffic percentage to the NN model to 0% and redistribute to GBT and LR until the NN issue is resolved. — Option C is correct because the immediate priority is to stop routing traffic to the failing NN model, which is causing timeouts and fallback to default predictions, thereby restoring accuracy quickly. By setting the NN endpoint's traffic percentage to 0% and redistributing to the healthy GBT and LR models, you eliminate the source of degraded predictions without requiring a redeployment or configuration change that could take time. This leverages Vertex AI's traffic split capability to isolate the faulty model while you diagnose and fix the latency issue.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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