The answer is a canary deployment with traffic splitting. This is correct because deploying two models to the same endpoint allows you to route a small percentage of inference requests to the new model while the majority continue to the stable model, enabling safe validation of performance before a full rollout. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of safe deployment strategies for generative AI models, often appearing in questions about endpoint configuration or traffic management. A common trap is confusing this with A/B testing for feature comparison, but canary deployment focuses on risk mitigation for a single new version. Remember the memory tip: “90/10 split, safe rollout fit”—the stable model gets 90% of traffic, the canary gets 10%, ensuring you can roll back quickly if the new model underperforms.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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.
Network Topology
Refer to the exhibit. A developer executed the command to list endpoints. They notice that two models are deployed to the same endpoint. What is the most likely reason for this configuration?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It is a canary deployment with traffic splitting
A is correct because deploying two models to the same endpoint with traffic splitting is a standard canary deployment strategy. In this configuration, a small percentage of inference requests are routed to the new model while the majority go to the stable model, allowing validation of the new model's performance before full rollout. This is commonly supported by model serving platforms like Amazon SageMaker, where you can specify a production variant with a traffic weight (e.g., 90%) and a canary variant with a lower weight (e.g., 10%).
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.
✓
It is a canary deployment with traffic splitting
Why this is correct
Multiple models on the same endpoint support gradual rollout by splitting traffic.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The endpoint is misconfigured and will cause conflicts
Why it's wrong here
Vertex AI supports multiple models per endpoint; this is not a misconfiguration.
✗
The models are from different frameworks
Why it's wrong here
Different frameworks do not prevent co-deployment on the same endpoint, but the exhibit does not indicate this.
✗
It is a batch prediction endpoint
Why it's wrong here
Batch prediction uses a single model; multiple models indicate online prediction with traffic splitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that deploying two models to the same endpoint is always an error, when in fact it is a deliberate pattern for canary testing or A/B testing with traffic splitting.
Detailed technical explanation
How to think about this question
Under the hood, canary deployments rely on the endpoint's routing logic to distribute requests based on variant weights, often implemented via a load balancer that reads the model variant name from the request metadata. In Amazon SageMaker, the `CreateEndpointConfig` API allows specifying multiple `ProductionVariants` with `InitialVariantWeight` values that sum to 1.0, and the endpoint automatically handles traffic distribution. A subtle behavior is that if the canary model fails health checks, the endpoint can automatically roll back traffic to the stable variant, ensuring high availability.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: It is a canary deployment with traffic splitting — A is correct because deploying two models to the same endpoint with traffic splitting is a standard canary deployment strategy. In this configuration, a small percentage of inference requests are routed to the new model while the majority go to the stable model, allowing validation of the new model's performance before full rollout. This is commonly supported by model serving platforms like Amazon SageMaker, where you can specify a production variant with a traffic weight (e.g., 90%) and a canary variant with a lower weight (e.g., 10%).
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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