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. 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.
An organization is deploying a generative AI model that requires GPU acceleration for inference. They are using OCI Data Science Model Deployment. The model is expected to handle variable traffic, with occasional spikes. Which scaling option should they configure to ensure cost-efficiency and responsiveness?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances
Option C is correct because autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.
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 OCI Generative AI on-demand API with a serverless endpoint
Why it's wrong here
On-demand API uses pre-built models; not for custom models deployed via Data Science.
✗
Use CPU-only instances and rely on batching
Why it's wrong here
CPU inference is too slow for large models.
✓
Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances
Why this is correct
Autoscaling matches capacity to load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy with a fixed number of 1 GPU instance
Why it's wrong here
Cannot handle spikes without overprovisioning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that serverless endpoints (Option A) are always the best for variable traffic, but candidates must recognize that OCI Generative AI on-demand API is a pre-built model service, not a custom model deployment, and thus does not support custom GPU scaling policies.
Detailed technical explanation
How to think about this question
OCI Data Science Model Deployment autoscaling uses a policy based on metrics such as CPU utilization, memory utilization, or custom metrics, and it can scale out by adding GPU instances (e.g., VM.GPU.A10.1 or BM.GPU4.8) and scale in by removing idle instances. The minimum and maximum instance counts define the scaling boundaries, and the cooldown periods prevent thrashing during rapid traffic changes. In a real-world scenario, a spike in inference requests from a chatbot during a marketing campaign would trigger scale-out within minutes, while idle periods at night would scale back to the minimum, optimizing GPU cost.
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: Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances — Option C is correct because autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.
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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