The answer is to increase the number of nodes in the dedicated AI cluster from 2 to 4. This is correct because sustained CPU utilization above 90% with a growing request queue indicates a compute bottleneck, not a code or model issue; scaling dedicated AI cluster nodes horizontally adds parallel processing capacity to handle the increased inference traffic without modifying the deployment. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding that dedicated AI clusters are designed for elastic scaling, and the common trap is to assume you need to optimize the model or upgrade instance shapes when the root cause is simply insufficient node count for the load. Remember the memory tip: when CPU is maxed and the queue grows, add nodes—not code.
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.
A company has deployed a fine-tuned GPT model on OCI Generative AI using a dedicated AI cluster with 2 nodes. The endpoint is used by an internal application that generates product descriptions. Recently, the application started receiving timeouts and slow responses. The monitoring dashboard shows that the cluster's CPU utilization is consistently above 90%, and the request queue is growing. The team has verified that the model and code have not changed. The application traffic has increased by 20% over the past month. What should the team do to resolve the issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of nodes in the dedicated AI cluster from 2 to 4.
Option D is correct because the dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.
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.
✗
Switch to a serverless endpoint to handle variable traffic.
Why it's wrong here
Serverless endpoints may have cold starts and higher per-request latency, not ideal for sustained load.
✗
Reduce the batch size in the inference requests to lower CPU usage.
Why it's wrong here
Reducing batch size may lower per-request CPU but overall throughput decreases, worsening the queue.
✗
Implement a caching layer for frequently requested descriptions.
Why it's wrong here
Caching reduces redundant requests but doesn't address the underlying capacity shortage for unique requests.
✓
Increase the number of nodes in the dedicated AI cluster from 2 to 4.
Why this is correct
This directly adds compute capacity to handle the increased traffic.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse reducing batch size (which actually increases CPU overhead per request) with reducing load, or assume caching is a universal performance fix, when the real solution is to scale the dedicated cluster horizontally to match increased traffic.
Detailed technical explanation
How to think about this question
Dedicated AI clusters in OCI Generative AI use GPU or CPU nodes for inference, and scaling out (adding nodes) distributes the inference workload across more compute units, reducing per-node CPU utilization. The request queue grows when the cluster's throughput (requests per second) is exceeded by the arrival rate; adding nodes increases the cluster's aggregate throughput linearly. In practice, monitoring metrics like P99 latency and queue depth help determine the exact scaling threshold, and OCI allows horizontal scaling of dedicated clusters via the console or API.
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: Increase the number of nodes in the dedicated AI cluster from 2 to 4. — Option D is correct because the dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.
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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