1Z0-1127-25 Practice Test 28 — 15 Questions

Question 1

Which LangChain component is responsible for splitting long documents into smaller, overlapping chunks before embedding?

Accepted Answer

Text Splitter. Text splitters (e.g., RecursiveCharacterTextSplitter) divide documents into chunks of a specified size with optional overlap. This ensures each chunk fits within the embedding model's token limit and preserves context at boundaries.

Answer

Retriever

Answer

Vector Store

Answer

Document Loader

Question 2

A company uses LangChain with OCI Generative AI. They notice that their agent-based application occasionally exceeds the rate limits of the OCI Generative AI service, causing errors. Which strategy is MOST effective for handling rate limits in a production LangChain application?

Accepted Answer

Implement a retry mechanism with exponential backoff when calling the model. Using a retry mechanism with exponential backoff is a standard and effective approach for handling rate limits.

Answer

Increase the k value in the retriever to reduce the number of API calls

Answer

Switch to a smaller model to reduce token consumption

Answer

Reduce the chunk_size parameter in text splitters

Question 3

When using LangChain's RetrievalQA chain with `chain_type="stuff"`, what happens if the retrieved documents exceed the model's context window?

Accepted Answer

The chain raises an error because the input is too long. The "stuff" chain type simply concatenates all retrieved documents into the prompt. If the total exceeds the context window, a TokenLimitError or similar error occurs. Other chain types like "map_reduce" or "refine" can handle larger contexts.

Answer

The chain automatically truncates the documents to fit

Answer

The chain uses a sliding window to summarize documents

Answer

The chain switches to a different model with larger context

Question 4

In Oracle AI Vector Search, which index type is designed for approximate nearest neighbor search and employs a hierarchical navigable small world graph, offering high recall and fast search speeds for high-dimensional data?

Accepted Answer

HNSW. HNSW (Hierarchical Navigable Small World) is a graph-based index that provides efficient approximate nearest neighbor search. IVF (Inverted File) uses clustering, and BTREE is for scalar data. VECTOR is a data type, not an index.

Answer

BTREE

Answer

IVF

Answer

VECTOR

Question 5

A developer is building a RAG pipeline using LangChain and Oracle AI Vector Search. After loading and splitting PDF documents, they generate embeddings and store them in Oracle Database using OracleVS. Which method should they call on the vector store object to create a retriever that uses similarity search with a configurable number of results?

Accepted Answer

as_retriever(). The as_retriever() method on a vector store returns a retriever object that can be configured with search_kwargs like 'k'.

Answer

from_texts()

Answer

max_marginal_relevance_search()

Answer

similarity_search()

Question 6

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 7

A developer wants to use LangChain to create an agent that can perform calculations and look up information from a database. Which tools should be provided to the agent?

Accepted Answer

Calculator tool and a custom tool for database queries. A calculator tool handles arithmetic, and a custom tool can be created to query the database. Web search is not needed, and an LLM is not a tool but the underlying model.

Answer

Custom tool for database queries and a vector store tool

Answer

Calculator tool and a retriever tool

Answer

Web search tool and an LLM tool

Question 8

A company uses OCI Generative AI Service to build a chatbot for customer support. They notice that the model sometimes generates inappropriate responses. What is the MOST effective way to mitigate this without retraining the model?

Accepted Answer

Configure system instructions to define acceptable behavior. Configuring system instructions is the most effective approach because it allows you to define the model's behavior and constraints at inference time without modifying the underlying model weights. In OCI Generative AI Service, system instructions act as a persistent prompt that guides the model's responses, enabling you to explicitly prohibit inappropriate content and enforce safety guidelines. This is a non-invasive, immediate mitigation that does not require the time, cost, or data preparation associated with retraining or fine-tuning.

Answer

Fine-tune the model with curated safe examples

Answer

Reduce the temperature parameter to 0

Answer

Use the moderation API to filter responses

Question 9

A model generates code with security issues. Which approach is best to mitigate this?

Accepted Answer

Add a system prompt with security guidelines. Adding a system prompt with security guidelines (option D) instructs the model to follow best practices, directly addressing security concerns without changing model training.

Answer

Reduce max_tokens

Answer

Increase temperature

Answer

Use a different model

Question 10

An enterprise is using OCI Generative AI with a RAG architecture. They observe that the LLM sometimes produces hallucinated answers that are not supported by the retrieved documents. Which strategy is most effective in reducing these hallucinations?

Accepted Answer

Provide clear instructions in the system prompt to answer only based on the provided context.. Explicitly instructing the LLM to answer only based on the provided context directly addresses the root cause of hallucinations in a RAG pipeline: the model's tendency to rely on its parametric knowledge rather than the retrieved documents. This system prompt acts as a behavioral constraint, forcing the model to ground its responses in the supplied context, which is the most effective and widely recommended mitigation strategy.

Answer

Increase the temperature parameter to make outputs more focused.

Answer

Use a smaller LLM to reduce model capacity.

Answer

Retrieve more chunks (increase top-k) to provide more context.

Question 11

A company wants to deploy a private instance of a large language model on OCI for sensitive data processing. What is the recommended approach?

Accepted Answer

Use OCI Dedicated AI Clusters for a private endpoint.. OCI Dedicated AI Clusters provide a fully isolated, private endpoint for deploying large language models, ensuring that sensitive data never traverses the public internet. This approach meets the requirement for private inference with no data leaving the customer's tenancy, unlike public endpoints or third-party hosting.

Answer

Use OCI Data Science with a publicly accessible model.

Answer

Use the OCI Generative AI public endpoint with data encryption.

Answer

Use third-party model hosting outside OCI.

Question 12

Which THREE are known challenges when deploying large language models in production?

Accepted Answer

Bias in training data perpetuating stereotypes. Large language models (LLMs) are trained on vast, unfiltered internet text corpora that inherently contain societal biases. These biases are learned and can be amplified during inference, leading to outputs that perpetuate harmful stereotypes, which is a well-documented production challenge.

Answer

Fast inference speed due to parallelization

Answer

Low memory footprint

Question 13

A research team is experimenting with few-shot prompting to improve a model's performance on a complex reasoning task. They find that the model's performance degrades when the few-shot examples are too similar to each other. What is the likely cause and best remedy?

Accepted Answer

The examples lack diversity, causing the model to overfit to a narrow pattern. Use more diverse examples.. When few-shot examples are too similar, the model overfits to a narrow pattern, reducing its ability to generalize to the diverse reasoning paths required by the task. This is a known limitation of in-context learning: the model treats the examples as a template rather than as diverse demonstrations. Using more diverse examples exposes the model to a wider range of reasoning patterns, improving robustness.

Answer

The model has not seen enough examples. Increase the number of few-shot examples.

Answer

The examples are presented in a confusing order. Reorder them by difficulty.

Answer

The temperature is too low, making the model too deterministic. Increase temperature slightly.

Question 14

A developer uses the OCI Generative AI Chat API to build a multi-turn conversational agent. They notice the model starts to lose context after several exchanges. What is the MOST likely cause?

Accepted Answer

The developer is not sending the conversation history with each request. The Chat API does not automatically manage conversation history; the developer must provide previous messages. The model itself retains state only within the provided context.

Answer

The temperature parameter is set too high, causing the model to forget

Answer

The model's context window is too small for the conversation length

Answer

The fine-tuning dataset did not include multi-turn examples

Question 15

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)

Answer

Increase the chunk size

Answer

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