- A
Embedding each chunk into a dense vector using an embedding model
Chunks are converted to vectors for similarity search.
- B
Retrieving relevant chunks based on cosine similarity to the query embedding
Retrieval step finds the most relevant chunks using vector similarity.
- C
Training a custom LLM from scratch on the document corpus
Why wrong: RAG uses an existing LLM; training from scratch is not required.
- D
Fine-tuning the generation model on the retrieved chunks
Why wrong: RAG does not fine-tune; it uses retrieved chunks as context in the prompt.
- E
Chunking documents into passages
Documents are split into manageable chunks for retrieval.
Question 922 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. 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 team is building a RAG pipeline on OCI. Which THREE steps are essential components of a standard RAG pipeline? (Select THREE)
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
✓
Embedding each chunk into a dense vector using an embedding model
Option A is correct because embedding each chunk into a dense vector using an embedding model is a fundamental step in a RAG pipeline. The embedding model converts text chunks into high-dimensional vector representations that capture semantic meaning, enabling efficient similarity search during retrieval. Without this step, the system cannot compare query intent with document content in a vector space.
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.
- ✓
Embedding each chunk into a dense vector using an embedding model
Why this is correct
Chunks are converted to vectors for similarity search.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Retrieving relevant chunks based on cosine similarity to the query embedding
Why this is correct
Retrieval step finds the most relevant chunks using vector similarity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Training a custom LLM from scratch on the document corpus
Why it's wrong here
RAG uses an existing LLM; training from scratch is not required.
- ✗
Fine-tuning the generation model on the retrieved chunks
Why it's wrong here
RAG does not fine-tune; it uses retrieved chunks as context in the prompt.
- ✓
Chunking documents into passages
Why this is correct
Documents are split into manageable chunks for retrieval.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between RAG's retrieval-augmented generation (which uses a frozen LLM with retrieved context) and fine-tuning or training a model, leading candidates to mistakenly select fine-tuning or training steps as essential components.
Detailed technical explanation
How to think about this question
In a RAG pipeline, the embedding model (e.g., text-embedding-ada-002 or all-MiniLM-L6-v2) produces fixed-length vectors, and cosine similarity is used to rank chunks by relevance to the query embedding. The chunking step (Option E) is critical because chunk size and overlap directly affect retrieval quality—too large chunks may dilute relevance, while too small chunks may lose context. Real-world implementations often use a chunk size of 256–512 tokens with 10–20% overlap to balance precision and recall.
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?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Embedding each chunk into a dense vector using an embedding model — Option A is correct because embedding each chunk into a dense vector using an embedding model is a fundamental step in a RAG pipeline. The embedding model converts text chunks into high-dimensional vector representations that capture semantic meaning, enabling efficient similarity search during retrieval. Without this step, the system cannot compare query intent with document content in a vector space.
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: Jul 4, 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.
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