- A
Switch from on-demand to dedicated AI cluster with batch endpoint.
Dedicated clusters provide lower cost per token for batch workloads and avoid contention.
- B
Reduce the max token limit for all requests.
Why wrong: Reducing tokens may affect output quality and does not address cost structure.
- C
Use a larger model to reduce retries.
Why wrong: Larger models cost more per token, not less.
- D
Increase the number of parallel requests to improve efficiency.
Why wrong: More parallel requests may increase cost due to higher throughput, not reduce it.
Question 796 of 991
Cost Optimization for Batch Inference
This 1Z0-1127 practice question tests your understanding of using oci generative ai service. 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 runs batch inference jobs daily using the OCI Generative AI service. The current cost is higher than expected. Which change would most effectively reduce cost while maintaining throughput?
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
✓
Switch from on-demand to dedicated AI cluster with batch endpoint.
Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.
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 from on-demand to dedicated AI cluster with batch endpoint.
Why this is correct
Dedicated clusters provide lower cost per token for batch workloads and avoid contention.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the max token limit for all requests.
Why it's wrong here
Reducing tokens may affect output quality and does not address cost structure.
- ✗
Use a larger model to reduce retries.
Why it's wrong here
Larger models cost more per token, not less.
- ✗
Increase the number of parallel requests to improve efficiency.
Why it's wrong here
More parallel requests may increase cost due to higher throughput, not reduce it.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that reducing token limits or increasing parallelism is the most effective cost-saving measure, when in fact the pricing model change from on-demand to dedicated infrastructure yields the greatest savings for predictable batch workloads.
Trap categories for this question
Command / output trap
Reducing tokens may affect output quality and does not address cost structure.
Detailed technical explanation
How to think about this question
Dedicated AI clusters in OCI Generative AI are provisioned with a fixed number of GPU nodes (e.g., A100 or H100) and billed hourly, making them cost-effective for predictable batch workloads where utilization is high. Batch endpoints queue requests and process them in a single job, reducing the overhead of individual API calls and allowing you to leverage lower batch pricing tiers. Under the hood, OCI uses a token-based billing model where on-demand requests incur a premium for elasticity, while dedicated clusters offer a flat-rate compute cost that becomes cheaper per token at scale.
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.
- →
Using OCI Generative AI Service — study guide chapter
Learn the concepts, then practise the questions
- →
Using OCI Generative AI Service practice questions
Targeted practice on this topic area only
- →
All 1Z0-1127 questions
991 questions across all exam domains
- →
Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 study guide
Full concept coverage aligned to exam objectives
- →
1Z0-1127 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related 1Z0-1127 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Prompt Engineering practice questions
Practise 1Z0-1127 questions linked to Prompt Engineering.
OCI Generative AI Service practice questions
Practise 1Z0-1127 questions linked to OCI Generative AI Service.
LLM Fundamentals practice questions
Practise 1Z0-1127 questions linked to LLM Fundamentals.
LangChain and AI Application Development practice questions
Practise 1Z0-1127 questions linked to LangChain and AI Application Development.
Fundamentals of Large Language Models practice questions
Practise 1Z0-1127 questions linked to Fundamentals of Large Language Models.
Using OCI Generative AI Service practice questions
Practise 1Z0-1127 questions linked to Using OCI Generative AI Service.
Building LLM Applications with RAG and Vector Search practice questions
Practise 1Z0-1127 questions linked to Building LLM Applications with RAG and Vector Search.
Deploying and Managing Generative AI on OCI practice questions
Practise 1Z0-1127 questions linked to Deploying and Managing Generative AI on OCI.
1Z0-1127 fundamentals practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 fundamentals.
1Z0-1127 scenario practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 scenario.
1Z0-1127 troubleshooting practice questions
Practise 1Z0-1127 questions linked to 1Z0-1127 troubleshooting.
Practice this exam
Start a free 1Z0-1127 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Switch from on-demand to dedicated AI cluster with batch endpoint. — Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on 1Z0-1127
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?
hard- A.Use the largest replica count.
- ✓ B.Manually scale down the cluster when not in use.
- C.Use the managed serving endpoint instead.
- D.Leave the cluster running continuously.
- ✓ E.Use the smallest possible model for the task.
Why B: Option B is correct because manually scaling down the dedicated AI cluster when not in use directly reduces compute costs by stopping idle GPU/CPU resources. In OCI Generative AI, dedicated AI clusters incur charges for provisioned capacity, so scaling down during inactivity avoids paying for unused infrastructure.
Variation 2. Which TWO factors are most important when deciding between on-demand and dedicated AI clusters for OCI GenAI?
medium- A.Fine-tuning capability
- B.Model size
- C.Data residency
- ✓ D.Number of concurrent requests
- ✓ E.Latency requirements
Why D: The number of concurrent requests (D) is critical because dedicated AI clusters provide guaranteed throughput and predictable performance for high-volume workloads, while on-demand clusters may throttle or queue requests under heavy load. Latency requirements (E) are equally important because dedicated clusters offer consistent low-latency inference by avoiding resource contention, whereas on-demand clusters can introduce variable latency due to shared infrastructure. Together, these factors directly determine whether a workload needs the isolation and guaranteed resources of a dedicated cluster or can tolerate the elasticity and potential variability of on-demand provisioning.
Keep practising
More 1Z0-1127 practice questions
- A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few t…
- A team is implementing a conversational chatbot that needs to remember a user's previous messages within the same sessio…
- A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored…
- A developer wants to deploy a RAG application using OCI Generative AI for both embedding and text generation while minim…
- A company wants to build a customer service chatbot that answers questions about their internal policy documents. The do…
- A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?
Last reviewed: Jun 30, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle 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 1Z0-1127 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.