- A
Serverless endpoint with fine-tuned model
Why wrong: Combines disadvantages of serverless and fine-tuning.
- B
Dedicated AI cluster with base model
Correct: Dedicated resources ensure low latency and high throughput.
- C
Dedicated AI cluster with fine-tuned model
Why wrong: Fine-tuning may not be necessary for summarization and could add overhead.
- D
Serverless endpoint with base model
Why wrong: Serverless may have cold starts and limited throughput, not ideal for low latency.
Question 126 of 991
1Z0-1127 Using OCI Generative AI Service Practice Question
This 1Z0-1127 practice question tests your understanding of using oci generative ai service. 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.
A company wants to use OCI Generative AI to summarize customer feedback. They need low latency and high throughput. Which configuration should they choose?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Dedicated AI cluster with base model
Dedicated AI clusters provide guaranteed compute resources (GPUs) with no multi-tenant contention, ensuring low latency and high throughput for inference workloads. Using a base model avoids the additional overhead of fine-tuning inference, which can introduce latency due to custom weight loading and optimization steps. This combination is optimal for real-time summarization of customer feedback where response time and volume are critical.
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.
- ✗
Serverless endpoint with fine-tuned model
Why it's wrong here
Combines disadvantages of serverless and fine-tuning.
- ✓
Dedicated AI cluster with base model
Why this is correct
Correct: Dedicated resources ensure low latency and high throughput.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Dedicated AI cluster with fine-tuned model
Why it's wrong here
Fine-tuning may not be necessary for summarization and could add overhead.
- ✗
Serverless endpoint with base model
Why it's wrong here
Serverless may have cold starts and limited throughput, not ideal for low latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that fine-tuned models always outperform base models for latency, when in fact fine-tuning adds inference overhead that can degrade performance for high-throughput, low-latency use cases.
Detailed technical explanation
How to think about this question
Under the hood, OCI Dedicated AI Clusters allocate isolated GPU nodes (e.g., NVIDIA A100 or H100) with dedicated networking and memory bandwidth, eliminating noisy-neighbor effects. Base models leverage pre-optimized inference kernels and caching, while fine-tuned models often require dynamic weight loading or LoRA adapters that add microseconds to each request. In real-world scenarios, a customer feedback pipeline processing thousands of reviews per second would see tail latency spikes on serverless endpoints due to cold starts or resource preemption, whereas a dedicated cluster maintains consistent sub-100ms response times.
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.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Dedicated AI cluster with base model — Dedicated AI clusters provide guaranteed compute resources (GPUs) with no multi-tenant contention, ensuring low latency and high throughput for inference workloads. Using a base model avoids the additional overhead of fine-tuning inference, which can introduce latency due to custom weight loading and optimization steps. This combination is optimal for real-time summarization of customer feedback where response time and volume are critical.
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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Last reviewed: Jun 30, 2026
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