1Z0-1127-25 Building LLM Applications with RAG and Vector Search Practice Test 8 — 15 Questions

Question 1

Refer to the exhibit. A developer runs the command and immediately tries to use the endpoint. The application fails with an error indicating the endpoint is not active. What is the most likely reason?

Accepted Answer

The endpoint is in provisioning state and not yet ready. When a developer creates a new model deployment endpoint in OCI, the endpoint enters a 'provisioning' state and can take several minutes to become active. Attempting to use the endpoint immediately after creation will result in an error indicating the endpoint is not ready, as the underlying infrastructure and model loading must complete before inference requests are accepted.

Answer

The model ID is not available in us-ashburn-1

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The purpose parameter is misspelled

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The compartment ID is incorrect

Question 2

Refer to the exhibit. A developer has set this policy to allow an OCI Data Science session to generate embeddings. However, the API call returns a 403 Forbidden. Which of the following is likely missing?

Accepted Answer

The dynamic group does not include the Data Science session. The 403 Forbidden error indicates that the Data Science session does not have the necessary permissions to call the API. In OCI, policies grant permissions to dynamic groups, not directly to resources. The dynamic group must include the Data Science session (e.g., by matching the session's OCID or resource type) for the policy to apply. Without this, the session is not recognized as a principal, and the API call is denied.

Answer

The policy needs a 'where request.region != ...' condition

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The policy should include 'in tenancy' instead of compartment

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The service requires 'manage' permission instead of 'use'

Question 3

A healthcare organization plans to deploy a RAG application on OCI that handles sensitive patient data. They require that all LLM inference and embedding processing happen within a controlled environment to avoid data leakage to public endpoints. Which OCI feature should they use?

Accepted Answer

OCI Dedicated AI Cluster. OCI Dedicated AI Cluster provides a fully isolated, single-tenant infrastructure for running AI workloads, including LLM inference and embedding processing. This ensures all data processing occurs within a controlled environment without traversing public endpoints, meeting the healthcare organization's requirement to prevent data leakage of sensitive patient data.

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OCI Data Labeling

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OCI Vault

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OCI Data Masking

Question 4

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

Accepted Answer

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space. OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

Answer

Use OCI Document Understanding to convert images to text, then index text

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Use separate vector stores for text and image embeddings

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Use image captioning to generate text descriptions and index those

Question 5

An organization wants to combine keyword search and vector search to improve retrieval accuracy in their RAG pipeline. Which OCI service provides built-in hybrid search capabilities?

Accepted Answer

OCI OpenSearch. OCI OpenSearch is the correct answer because it natively supports hybrid search, which combines keyword-based (BM25) and vector-based (k-NN) queries in a single search request. This allows the RAG pipeline to retrieve documents that match both exact terms and semantic meaning, improving overall accuracy without requiring separate search systems.

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OCI Search with AI

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Autonomous Database with AI Vector Search

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OCI Logging

Question 6

A RAG application is hallucinating because the LLM receives irrelevant context from the retrieval step, even when topK is set to 3. Which strategy would best reduce hallucination by improving the relevance of retrieved documents?

Accepted Answer

Add a reranking step after retrieval to select the most relevant chunks. Adding a reranking step after retrieval directly addresses the core issue: even with a low topK, the initial retrieval may return chunks that are semantically similar but not precisely relevant to the query. Reranking uses a cross-encoder model to score each retrieved chunk against the query, reordering them so that only the most contextually relevant chunks are passed to the LLM. This reduces the chance of the LLM receiving irrelevant context, thereby minimizing hallucination.

Answer

Reduce the chunk size to one sentence per chunk

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Implement a query rewriting mechanism

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Increase topK to 10 to provide more context

Question 7

A developer notices that the RAG application returns irrelevant chunks for user queries. The embedding model used is `cohere.embed-english-light-v3.0`. Which action is MOST likely to improve relevance?

Accepted Answer

Switch to a larger embedding model (e.g., cohere.embed-english-v3.0). The `cohere.embed-english-light-v3.0` model is a smaller, faster embedding model that may lack the semantic richness needed to capture nuanced query-document relationships. Switching to the larger `cohere.embed-english-v3.0` model provides higher-dimensional embeddings with better representational capacity, which directly improves the relevance of retrieved chunks in a RAG pipeline.

Answer

Reduce the number of retrieved chunks (k)

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Increase the chunk size

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Use a different similarity metric (e.g., Euclidean instead of cosine)

Question 8

A developer wants to implement a simple RAG pipeline using OCI Language's text generation and embedding models. Which OCI SDK method is used to generate embeddings for a text chunk?

Accepted Answer

embed_text. The correct OCI SDK method for generating embeddings for a text chunk is `embed_text`. This method is part of the OCI Language service's `AIServiceLanguageClient` and directly returns vector representations of input text, which are essential for RAG pipelines to enable semantic search and retrieval.

Answer

generate_embeddings

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encode_text

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create_embedding

Question 9

A team uses Cohere's `rerank` endpoint after initial retrieval to improve result quality. What is the main benefit of reranking?

Accepted Answer

It reorders chunks by relevance to the query. The rerank endpoint takes the initial set of retrieved chunks and re-scores them based on their semantic relevance to the query, producing a reordered list where the most contextually appropriate chunks appear first. This improves the quality of the input fed to the LLM, leading to more accurate and coherent generated responses without altering the embeddings or the retrieval count.

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It generates new embeddings for chunks

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It combines multiple queries

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It reduces the number of retrieved chunks

Question 10

Which TWO of the following are valid similarity metrics used in vector search?

Accepted Answer

Cosine similarity. Cosine similarity measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude. It is widely used in vector search for comparing embeddings because it effectively captures semantic similarity in high-dimensional spaces, such as those produced by LLMs.

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Levenshtein distance

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Hamming distance

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Jaccard index

Question 11

Which THREE factors should be considered when designing a chunking strategy for a RAG application?

Accepted Answer

Desired granularity of retrieval. The desired granularity of retrieval determines how much context is returned per chunk. Fine-grained chunks (e.g., sentences) improve precision for specific answers, while coarse chunks (e.g., paragraphs) provide broader context. This directly impacts the relevance and completeness of the retrieved passages in a RAG pipeline.

Answer

Number of GPUs available

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Database indexing method

Question 12

A startup is building a customer support chatbot using RAG with OCI Generative AI. They have a large corpus of FAQ documents stored as PDFs in OCI Object Storage. The developer uses OCI Language to embed the text and stores vectors in OCI OpenSearch. During testing, the chatbot often fails to answer questions because relevant FAQ entries are not retrieved. The team suspects the chunking size is too large, causing loss of specific details. After reducing chunk size, retrieval improves slightly but still misses many answers. What should the team do NEXT?

Accepted Answer

Use a sliding window chunking strategy with overlap. A sliding window chunking strategy with overlap ensures that context is preserved across chunk boundaries, preventing the loss of specific details that can occur when a relevant sentence or phrase is split between two chunks. This directly addresses the symptom where reducing chunk size alone still misses answers, as overlapping chunks increase the likelihood that the exact text needed for retrieval appears in at least one chunk.

Answer

Increase the number of retrieved chunks (k)

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Switch to a different embedding model

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Manually rephrase the queries

Question 13

A financial services company is deploying a RAG system for regulatory compliance queries. The system uses OCI Data Science to run a custom embedding model fine-tuned on regulatory documents. The index in OpenSearch uses cosine similarity and HNSW algorithm. Users report that queries containing synonyms to regulatory terms (e.g., "AML" vs "Anti-Money Laundering") often fail to retrieve relevant documents. Which combination of improvements would be MOST effective? (Assume budget and latency constraints)

Accepted Answer

Implement a hybrid search combining keyword and vector search. Hybrid search (combining keyword (BM25) and vector search) catches exact synonym matches from text. Query expansion helps but may not be as reliable. Fine-tuning on synonyms is possible but time-consuming. Increasing HNSW m slightly improves recall but does not address synonym gap.

Answer

Increase the `m` parameter in HNSW to improve recall

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Fine-tune the embedding model further on a dataset of synonyms

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Use query expansion with a thesaurus before embedding

Question 14

An enterprise RAG application experiences high latency during peak hours. The architecture uses OCI OpenSearch with a single node cluster storing 5 million vectors (768 dimensions). The search uses exact k-NN (EF_SEARCH=500). The average query takes 1.5 seconds, but the SLA requires <500ms. The team considers several options: A) Switch to ANN with lower recall (HNSW with ef_search=50), B) Scale OpenSearch cluster to 3 nodes, C) Reduce embedding dimension to 256 using PCA, D) Increase the number of shards from 1 to 10. Which option provides the best balance of latency reduction and minimal impact on retrieval quality? (Assume all options are feasible)

Accepted Answer

Increase the number of shards from 1 to 10. Increasing shards on the same node partitions the index, so each shard contains fewer vectors, making exact search faster. This reduces latency without sacrificing accuracy. ANN reduces recall, scaling adds cost and complexity, and dimension reduction can degrade embedding quality.

Answer

Scale OpenSearch cluster to 3 nodes

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Switch to ANN (HNSW with ef_search=50)

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Reduce embedding dimension to 256 using PCA

Question 15

A developer is building a RAG application using OCI Generative AI. They notice that the generated responses often contain outdated information even though the knowledge base is updated daily. What is the most likely cause?

Accepted Answer

The vector database index is not rebuilt after data updates.. In a RAG pipeline, the vector database index is a static snapshot of the embedded knowledge base. When the knowledge base is updated daily, the index must be rebuilt or incrementally updated to reflect the new data. Without rebuilding, the retrieval step will still search the old index, returning outdated chunks and causing the LLM to generate stale responses.

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The embedding model is not fine-tuned on the latest data.

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The retrieval top-k is set too high.

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The chunk size is too small, causing loss of context.