Question 824 of 991

Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

1Z0-1127 Fundamentals of Large Language Models Practice Question

This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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.

An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Dedicated fine-tuned endpoint per tenant.
Why wrong: Dedicated endpoints are costly and do not leverage shared infrastructure.
B
Shared base model with per-tenant system prompts and retrieval.
This approach uses a shared model with tenant-specific prompts and RAG, balancing cost and isolation.
C
On-premises deployment of open-source models.
Why wrong: On-premises deployment shifts operational burden and may not provide the same scalability or integration with OCI.
D
Single large fine-tuned model with conditional logic.
Why wrong: A single model with conditional logic is prone to prompt injection and lacks proper data isolation.

Full breakdown with real-world context →

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

✓

Shared base model with per-tenant system prompts and retrieval.

Option B is correct because it leverages a shared base model with per-tenant system prompts and retrieval-augmented generation (RAG) to isolate custom instructions and data without the cost of dedicated endpoints. This approach minimizes compute overhead by reusing a single model instance while maintaining logical isolation through prompt engineering and vector-based retrieval, aligning with OCI's pay-as-you-go pricing model.

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.

✗
Dedicated fine-tuned endpoint per tenant.
Why it's wrong here
Dedicated endpoints are costly and do not leverage shared infrastructure.
✓
Shared base model with per-tenant system prompts and retrieval.
Why this is correct
This approach uses a shared model with tenant-specific prompts and RAG, balancing cost and isolation.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
On-premises deployment of open-source models.
Why it's wrong here
On-premises deployment shifts operational burden and may not provide the same scalability or integration with OCI.
✗
Single large fine-tuned model with conditional logic.
Why it's wrong here
A single model with conditional logic is prone to prompt injection and lacks proper data isolation.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume fine-tuning is necessary for customization, overlooking that system prompts and retrieval can achieve equivalent isolation at a fraction of the cost, which the Oracle OCI GenAI exam tests by contrasting dedicated endpoints against shared-model strategies.

Detailed technical explanation

How to think about this question

Under the hood, RAG uses vector embeddings stored in OCI OpenSearch or a similar vector store to retrieve tenant-specific context at inference time, while system prompts inject custom instructions into the model's context window without altering weights. This approach avoids the overhead of fine-tuning (which requires GPU-intensive training and separate model copies) and leverages OCI's serverless AI inference endpoints, which scale to zero when idle. A real-world scenario is a legal document assistant where each law firm has proprietary clause libraries; RAG ensures only relevant clauses are retrieved per query, maintaining data privacy without dedicated models.

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.

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FAQ

Questions learners often ask

What does this 1Z0-1127 question test?

Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Shared base model with per-tenant system prompts and retrieval. — Option B is correct because it leverages a shared base model with per-tenant system prompts and retrieval-augmented generation (RAG) to isolate custom instructions and data without the cost of dedicated endpoints. This approach minimizes compute overhead by reusing a single model instance while maintaining logical isolation through prompt engineering and vector-based retrieval, aligning with OCI's pay-as-you-go pricing model.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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