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1Z0-1127-25 · topic practice

Building LLM Applications with RAG and Vector Search practice questions

Practise Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127-25 Building LLM Applications with RAG and Vector Search practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: Building LLM Applications with RAG and Vector Search
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What the exam tests

What to know about Building LLM Applications with RAG and Vector Search

Building LLM Applications with RAG and Vector Search questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Building LLM Applications with RAG and Vector Search exam traps

  • ▸Answering from memory before reading the full scenario.
  • ▸Missing a constraint such as cost, availability, security, scope or command context.
  • ▸Choosing a broad answer when the question asks for the most specific fix.
  • ▸Ignoring why the wrong options are tempting.

Practice set

Building LLM Applications with RAG and Vector Search questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice
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A developer is building a RAG chatbot for an internal knowledge base. To ensure the system retrieves the most relevant chunks, what is the best practice for chunking?

Question 2easymultiple choice
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A company uses OCI Generative AI to create embeddings for a vector search. They notice high latency in search queries. What is one possible optimization?

Question 3easymultiple choice
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An application uses RAG to answer customer queries, but answers are often incomplete because the retrieved chunks do not contain full context. Which adjustment should the developer make?

Question 4mediummultiple choice
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A team uses OCI OpenSearch as a vector database for RAG. Some queries return no results despite relevant documents being indexed. What is a likely cause?

Question 5hardmultiple choice
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A developer uses OCI Generative AI with a custom OCI OpenSearch vector store. The text generation model sometimes hallucinates facts not in the retrieved documents. What is the most effective mitigation?

Question 6mediummulti select
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Which THREE of the following are likely causes if retrieval returns no results despite documents being indexed in an OCI OpenSearch vector store?

Question 7easymultiple choice
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Refer to the exhibit. Why did the embedding creation fail?

Network Topology
oci generative-ai embedding createmodel-id "cohere.embed-english-v3.0"input-text "Hello world"region us-ashburn-1Output: Error: MissingAuthenticationError
Question 8mediummultiple choice
Read the full Building LLM Applications with RAG and Vector Search explanation →

Refer to the exhibit. What is a potential issue with this OCI OpenSearch index template configuration?

Exhibit

{
  "version": "1.0",
  "index_patterns": ["*"],
  "priority": 20,
  "template": {
    "settings": {
      "number_of_shards": 1,
      "number_of_replicas": 0,
      "index.knn": true,
      "index.knn.space_type": "l2"
    },
    "mappings": {
      "properties": {
        "content_embedding": {
          "type": "knn_vector",
          "dimension": 1024,
          "method": {
            "name": "hnsw",
            "space_type": "cosinesimil",
            "engine": "lucene",
            "parameters": {
              "ef_construction": 512,
              "m": 32
            }
          }
        }
      }
    }
  }
}
Question 9hardmultiple choice
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Refer to the exhibit. What is the best action to resolve this error?

Exhibit

Error: The total token count (4082) exceeds the model's maximum context length (4096). The input includes 512 tokens for system prompt, 3072 tokens for retrieved documents, and 498 tokens for the user query.
Question 10easymultiple choice
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A company is building a RAG application for customer support. The knowledge base includes documents in English, Spanish, and French. Which embedding model should they use from OCI Generative AI to ensure accurate retrieval across all languages?

Question 11mediummultiple choice
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An organization is experiencing low recall in their RAG system. They are using OCI OpenSearch as the vector store with cosine similarity. After reviewing the retrieved chunks, they notice that relevant documents are not being returned. Which configuration change is most likely to improve recall?

Question 12hardmultiple choice
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A healthcare company is deploying a RAG application using OCI Generative AI and wants to ensure patient data privacy. They cannot send sensitive data to a public embedding endpoint. Which approach should they take to embed documents while maintaining data residency and security?

Question 13mediummultiple choice
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During a RAG implementation, the response quality degrades because the LLM receives too many irrelevant document chunks. Which technique can best filter out irrelevant chunks before sending them to the LLM?

Question 14hardmultiple choice
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A company is using Oracle Database 23ai AI Vector Search for their RAG pipeline. They notice that similarity search often returns chunks that are semantically unrelated but syntactically similar due to token overlap. Which vector index type should they consider to improve semantic relevance?

Question 15mediummultiple choice
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A team is designing a RAG system for legal document review. They want to ensure that the retrieved chunks are contextually coherent and not truncated mid-sentence. Which chunking strategy should they use?

Question 16mediummulti select
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Which TWO of the following are best practices when indexing documents for a RAG application using OCI OpenSearch?

Question 17hardmulti select
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Which THREE factors should be considered when choosing a vector store for a RAG application in OCI?

Question 18easymultiple choice
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A developer sends the above request to the OCI Generative AI API. The response returns an error: 'InvalidParameter: The parameter 'topP' is not supported for this model.' What is the most likely reason?

Exhibit

Refer to the exhibit.

```json
{
  "modelId": "cohere.command-r-plus-v1:0",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ],
  "parameters": {
    "temperature": 0.5,
    "topP": 0.9
  }
}
```
Question 19hardmultiple choice
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A developer implements a RAG chatbot using OCI Generative AI with streaming enabled. The chatbot fails to remember earlier conversation turns during a session. What is the most likely cause?

Question 20mediummultiple choice
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A data scientist is designing a RAG system with a large vector database (hundreds of millions of documents) and requires high recall accuracy. Which vector search index type should be used in OCI Search with OpenSearch?

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Frequently asked questions

What does the 1Z0-1127-25 exam test about Building LLM Applications with RAG and Vector Search?
Building LLM Applications with RAG and Vector Search questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Building LLM Applications with RAG and Vector Search questions in a focused session?
Yes — the session launcher on this page draws every question from the Building LLM Applications with RAG and Vector Search domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other 1Z0-1127-25 topics?
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Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the 1Z0-1127-25 exam covers. They are not copied from any real exam or dump site.