1Z0-1127 Practice Test 16 — 15 Questions

Question 1

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?

Accepted Answer

The 'cohere.command-r-plus-v1:0' model does not accept the topP parameter.. Option B is correct because the command-r-plus model does not support the topP parameter; it only supports temperature. Option A is wrong because topP is a valid parameter in some models. Option C is wrong because JSON is valid. Option D is wrong because modelId is correct.

Answer

The model ID is deprecated.

Answer

The topP parameter value is out of range.

Answer

The JSON request format is incorrect.

Question 2

What is the primary purpose of an embedding model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors.. Embedding models convert text into dense vector representations that can be used for similarity search in a vector database.

Answer

To generate human-like responses.

Answer

To rank search results.

Answer

To summarize long documents.

Question 3

A legal firm needs an AI assistant that can answer questions based on a large corpus of internal regulations that change quarterly. The firm also requires high accuracy and the ability to cite sources. Which approach should the firm choose?

Accepted Answer

Build a RAG application with vector search and citation generation. A RAG application with vector search retrieves relevant regulations and allows citation generation, keeping answers up-to-date without retraining.

Answer

Use a pre-trained model without customization

Answer

Implement a rule-based search engine

Answer

Fine-tune a pre-trained model on the current regulations

Question 4

A company is building a RAG application on OCI and needs a managed vector database with native support for AI Vector Search, which offers high performance and integration with OCI GenAI. Which OCI service should they use?

Accepted Answer

Autonomous Database with AI Vector Search. Oracle Autonomous Database provides AI Vector Search, enabling efficient similarity search on vector embeddings. OCI OpenSearch can also serve as a vector store but lacks the same level of AI Vector Search integration. MySQL HeatWave and Object Storage are not optimized for vector search.

Answer

OCI OpenSearch

Answer

MySQL HeatWave

Answer

OCI Object Storage

Question 5

Which TWO best practices should be followed when designing a RAG application using OCI GenAI? (Select two.)

Accepted Answer

Use OCI Dedicated AI Cluster for inference to ensure data privacy. Using dedicated AI endpoints (A) ensures isolation and performance. Monitoring with Vault (B) is good for secrets, not logs. Using inference on dedicated AI clusters (D) is a best practice. Batching queries (C) is fine but not top. Storing raw documents (E) is unnecessary.

Answer

Batch all user queries to minimize costs

Answer

Store raw documents in the vector database for easy updates

Answer

Store embedding API keys in OCI Vault and rotate frequently

Question 6

A security audit reveals that the RAG application exposes internal documents through the chatbot. The vector search index contains sensitive data. Which action should be taken FIRST to mitigate?

Accepted Answer

Implement access control at the OpenSearch index level. Implementing access control at the OpenSearch index level prevents unauthorized users from retrieving sensitive documents. Redaction reduces risk but is less comprehensive. Changing model or reducing chunks does not address the exposure.

Answer

Reduce the number of retrieved chunks

Answer

Redact sensitive terms from documents before embedding

Answer

Use a different embedding model

Question 7

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.. Option C is correct because if the vector index is not rebuilt after data updates, retrieval will still return old chunks. Option A is wrong because fine-tuning the embedding model is not required for updating knowledge. Option B is wrong because chunk size affects context but not freshness. Option D is wrong because a high top-k would include more results, but still old if not updated.

Answer

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

Answer

The retrieval top-k is set too high.

Answer

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

Question 8

A company is deploying a RAG application for legal document analysis using OCI. Which three best practices should be followed to mitigate hallucinations? (Choose 3.)

Accepted Answer

Implement a fallback to abstain from answering if confidence is low.. Options A, B, and E are correct. Providing source citations keeps the model grounded, low temperature reduces randomness, and a fallback to abstain avoids false answers. Option C is possible but adds complexity and latency. Option D increases noise and may increase hallucinations.

Answer

Include a verification step via a secondary model.

Answer

Increase the number of retrieved chunks to 10.

Question 9

A developer is building a RAG pipeline using OCI Data Science and wants to store vector embeddings. Which OCI service is optimized for vector search and can be used as a vector store?

Accepted Answer

OCI OpenSearch. Option B is correct because OCI OpenSearch provides a vector engine that supports k-NN search. Option A is wrong because OCI Object Storage is not a searchable vector store. Option C is wrong because OCI Autonomous Database has vector capabilities but is not primarily optimized for vector search at scale. Option D is wrong because OCI Streams is for streaming data.

Answer

OCI Autonomous Database

Answer

OCI Object Storage

Answer

OCI Streaming

Question 10

An OCI CLI command above returns embeddings for the phrase 'Hello world'. The developer notices that the embedding vector length is 384 dimensions. However, they expected 768 dimensions. What is the most likely cause?

Accepted Answer

The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.. Option B is correct because cohere.embed-multilingual-light-v3.0 outputs 384-dimensional embeddings by default, while the 'v3' version outputs 1024. Option A is wrong because the flag does not affect dimension. Option C is wrong because truncate mode does not change dimension. Option D is wrong because input length is irrelevant.

Answer

The input text 'Hello world' is too short, causing dimension reduction.

Answer

The CLI result is truncated in the display.

Answer

The --truncate END flag reduces the dimension.

Question 11

A RAG system returns irrelevant chunks even though the embedding model and vector index are correctly configured. After reviewing, the chunks are too large and contain extraneous information. Which combination of adjustments should be made to improve relevance?

Accepted Answer

Reduce chunk size, increase overlap, and adjust topK.. Adjusting chunk size, chunk overlap, and topK all influence retrieval quality. A holistic tuning is often needed to address irrelevant chunks.

Answer

Increase chunk overlap only.

Answer

Decrease chunk size and increase chunk overlap.

Answer

Use semantic chunking and adjust topK.

Question 12

A real-time customer support chatbot uses RAG with OCI Generative AI. The average response time is 5 seconds, which is too slow. The team identifies the vector search as the bottleneck. Which optimization would most reduce latency?

Accepted Answer

Use approximate nearest neighbor (ANN) search with a lower recall setting. Using approximate nearest neighbor (ANN) search with a lower recall setting reduces search time by trading off some accuracy for speed, which is often acceptable in real-time applications.

Answer

Increase the number of retrieved documents to 10

Answer

Move the vector store to a different region

Answer

Switch to a larger embedding model for better accuracy

Question 13

A development team notices that their RAG application returns responses slowly when processing large PDF documents (100+ pages). They need to improve response time without significantly reducing retrieval quality. Which action is most effective?

Accepted Answer

Use a smaller chunk size during document ingestion. Using smaller chunk sizes reduces the amount of text per embedding, speeding up retrieval and subsequent processing. Increasing topK would retrieve more contexts and slow down response. Switching to a more expensive model or adding a reranker would increase latency.

Answer

Add a reranking step after initial retrieval

Answer

Increase the topK parameter to retrieve more context

Answer

Switch to a larger embedding model for better accuracy

Question 14

Which THREE components are essential in a typical RAG architecture built on OCI? (Select three.)

Accepted Answer

Vector database (e.g., OCI OpenSearch, Autonomous Database). A vector store (A) for similarity search, an LLM (B) for generating answers, and a prompt template (E) to combine context and query. Embedding model (D) is also essential (but not listed as a separate option? Actually D is embedding model, so that is also essential. But we need exactly three. The correct ones are A, B, D. A vector store, an LLM, and an embedding model are core. Prompt template is also core, but we have to select three. Let's adjust: Options: A: vector store, B: LLM, C: data pipeline, D: embedding model, E: prompt template. Essential: A, B, D. Prompt template is important but not strictly essential if prompt is hardcoded. Data pipeline is important but not part of runtime RAG. So A, B, D.

Answer

Data ingestion pipeline with Apache Spark

Answer

Prompt template for system instructions

Question 15

What is the primary purpose of chunking documents in a RAG pipeline?

Accepted Answer

To ensure each chunk fits within the model's context window. Chunking ensures that each text segment fits within the input token limit of the embedding model and the LLM context window. While it may also help retrieval granularity, the primary reason is to meet model constraints.

Answer

To improve embedding quality

Answer

To speed up training

Answer

To reduce storage costs

1Z0-1127 Practice Test 16 — 15 Questions

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1Z0-1127 Practice Test 16 — 15 Questions

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