Quick Answer
The correct answer is deploying models on dedicated VMs in a specific region and using the regional endpoint for Vertex AI. These two features enforce data residency by ensuring that all compute resources and API calls remain within the chosen geographic boundary, preventing any data egress to other regions. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to architect for regulatory compliance, often appearing as a scenario where a company must satisfy GDPR or local data sovereignty laws. A common trap is confusing regional endpoints with globally distributed services like Cloud CDN or Global Load Balancer, which explicitly route traffic across regions and violate residency. For a quick memory tip, think “dedicated and local” — dedicated VMs keep compute local, and the regional endpoint keeps API calls local, forming a two-part lock on your data’s location.
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 company is building a generative AI application that must adhere to strict data residency regulations. Which TWO Google Cloud features can help ensure that data does not leave a specific geographic region?
Using the regional endpoint for Vertex AI
Regional endpoints ensure API calls stay within the region.
Vertex AI Model Caching
Why wrong: Caching does not guarantee data residency.
Global load balancer with Cloud Armor
Why wrong: Load balancer can route traffic globally.
Cloud CDN for content delivery
Why wrong: CDN distributes content globally.
Deploying models on dedicated VMs in a specific region
Dedicated VMs can be confined to a region.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Using the regional endpoint for Vertex AI
Options B and D are correct. Dedicated VMs and the regional endpoint for Vertex AI ensure data stays in a specified region. Option A is wrong because model caching may use regional resources but does not enforce residency. Option C is wrong because Cloud CDN distributes data globally. Option E is wrong because Global Load Balancer routes traffic globally.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Using the regional endpoint for Vertex AI
Why this is correct
Regional endpoints ensure API calls stay within the region.
Related concept
Read the scenario before looking for a memorised answer.
Vertex AI Model Caching
Why it's wrong here
Caching does not guarantee data residency.
Global load balancer with Cloud Armor
Why it's wrong here
Load balancer can route traffic globally.
Cloud CDN for content delivery
Why it's wrong here
CDN distributes content globally.
Deploying models on dedicated VMs in a specific region
Why this is correct
Dedicated VMs can be confined to a region.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
The correct answer is: Using the regional endpoint for Vertex AI — Options B and D are correct. Dedicated VMs and the regional endpoint for Vertex AI ensure data stays in a specified region. Option A is wrong because model caching may use regional resources but does not enforce residency. Option C is wrong because Cloud CDN distributes data globally. Option E is wrong because Global Load Balancer routes traffic globally.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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