The answer is to change the deployment to a different GPU shape, such as VM.GPU.A10.2. This resolves the GPU capacity error because the "Insufficient capacity for shape" message indicates that the specific VM.GPU.A10.1 shape lacks available resources in the current availability domain, not that your tenancy’s service limits are exhausted. By switching to a different GPU shape within the same family, you bypass the localized capacity bottleneck without needing to change regions or request a limit increase, making it the fastest fix. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding of OCI Data Science model deployment troubleshooting, where a common trap is to assume you must increase service limits or move to a different AD. Remember the memory tip: "Shape shift, not region lift"—when you see a capacity error, try a different shape first.
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. 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 organization uses OCI Data Science to train a generative AI model for code generation. After training, you want to deploy it as a REST API. You create a model deployment using the OCI console, but after 30 minutes the deployment status is still 'Creating'. You check the logs and see the message: 'Insufficient capacity for shape VM.GPU.A10.1 in availability domain AD-1'. The deployment is configured with a single replica. You have verified your tenancy has sufficient service limits for GPU instances. What should you do to resolve this issue quickly?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the deployment to use a different GPU shape, such as VM.GPU.A10.2
Option A is correct because the error indicates that the specific GPU shape VM.GPU.A10.1 lacks capacity in the current availability domain. Switching to a different GPU shape, such as VM.GPU.A10.2, which uses a different instance configuration, can bypass the capacity constraint without requiring a region change or service limit increase. This is the fastest resolution because it directly addresses the availability domain capacity issue while keeping the deployment in the same region and AD.
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.
✓
Change the deployment to use a different GPU shape, such as VM.GPU.A10.2
Why this is correct
A different GPU shape may have available capacity in the same availability domain.
Related concept
Read the scenario before looking for a memorised answer.
✗
Delete the deployment and create it in a different region with more GPU capacity
Why it's wrong here
This is a drastic step and may introduce latency; not the first action.
✗
Request a service limit increase for GPU shapes
Why it's wrong here
Service limits are not the issue; the error is about capacity, not limits.
✗
Wait for 1 hour and check again; capacity may become available
Why it's wrong here
Capacity shortages may persist; waiting is not a reliable solution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse service limits with capacity availability, assuming a limit increase will fix the issue, when in fact the error explicitly states 'Insufficient capacity' for the shape, not a limit breach.
Detailed technical explanation
How to think about this question
OCI Data Science model deployments use GPU shapes like VM.GPU.A10.1 and VM.GPU.A10.2, which are based on NVIDIA A10 Tensor Core GPUs. The capacity error arises from the underlying bare-metal or VM host availability in a specific availability domain; OCI allocates GPU capacity per AD, and a shape like VM.GPU.A10.1 may be oversubscribed while VM.GPU.A10.2 (with two GPUs) has available capacity. In practice, switching to a shape with more GPUs can also improve inference throughput for large models, but the primary goal here is to resolve the deployment creation failure quickly.
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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the deployment to use a different GPU shape, such as VM.GPU.A10.2 — Option A is correct because the error indicates that the specific GPU shape VM.GPU.A10.1 lacks capacity in the current availability domain. Switching to a different GPU shape, such as VM.GPU.A10.2, which uses a different instance configuration, can bypass the capacity constraint without requiring a region change or service limit increase. This is the fastest resolution because it directly addresses the availability domain capacity issue while keeping the deployment in the same region and AD.
What should I do if I get this 1Z0-1127 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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Question Discussion
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