1Z0-1127-25 Practice Test 27 — 15 Questions

Question 1

When tuning the temperature parameter for a text generation task, which effect does setting temperature to 0.1 have compared to 0.9?

Accepted Answer

It makes outputs more focused and deterministic. Low temperature makes output more deterministic and repetitive; high temperature increases randomness and creativity.

Answer

It increases the maximum number of tokens generated

Answer

It reduces the vocabulary considered at each step

Answer

It increases randomness, producing more diverse outputs

Question 2

An organization wants to ensure that prompts submitted to an LLM do not contain sensitive customer data. Which practice is most effective?

Accepted Answer

Sanitize user inputs by removing sensitive information before including them in the prompt. Sanitizing prompts before submission (e.g., removing PII, using placeholders) prevents sensitive data from being sent to the model. Other options either do not prevent data leakage or are less direct.

Answer

Use a low temperature to avoid generating sensitive data

Answer

Increase the max tokens to allow the model to ignore sensitive data

Answer

Implement a prompt injection detection system that blocks malicious prompts

Question 3

In LangChain, which class should be used to wrap Oracle Cloud Infrastructure's Generative AI service as a chat model?

Accepted Answer

ChatOCIGenAI. ChatOCIGenAI is the LangChain wrapper for chat-based models from OCI Generative AI. OCIGenAI is for LLM (non-chat) completions, OCIGenAIEmbeddings is for embeddings, and OracleVS is for vector store integration with Oracle Database.

Answer

OracleVS

Answer

OCIGenAIEmbeddings

Answer

OCIGenAI

Question 4

A developer is using LangChain's RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200. Which statement best describes the resulting chunks?

Accepted Answer

Chunks are at most 1000 characters, and consecutive chunks overlap by 200 characters. With chunk_size=1000 and chunk_overlap=200, each chunk is up to 1000 characters and consecutive chunks share 200 characters of overlap to preserve context across boundaries. The splitter recursively tries to split on separators like newlines to keep chunks semantically coherent.

Answer

Chunks are at most 1200 characters, overlapping by 200

Answer

Chunks are exactly 800 characters, with no overlap

Answer

Each chunk is exactly 1000 characters, with no overlap

Question 5

Which LangChain document loader should be used to load text from a web page given its URL?

Accepted Answer

WebBaseLoader. WebBaseLoader is a LangChain document loader that fetches a web page from a URL and extracts its text content. PDFLoader loads PDFs, TextLoader loads local text files, and CSVLoader loads CSV files.

Answer

CSVLoader

Answer

PDFLoader

Answer

TextLoader

Question 6

Which of the following best describes the role of a Retriever in a LangChain RAG pipeline?

Accepted Answer

It fetches relevant document chunks from a vector store based on a query. A Retriever is responsible for fetching relevant documents from a vector store based on a query. It abstracts the search logic so that downstream components (like LLMChain) can use the retrieved context. Document loaders handle input, embeddings convert text to vectors, and splitters divide documents.

Answer

It splits documents into smaller chunks

Answer

It loads documents from various sources like PDF or HTML

Answer

It encodes documents into dense vector representations

Question 7

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. The OCI Java SDK provides native Java support for calling OCI services, including OCI Generative AI, making it the appropriate choice for a Java application. Options A and B are SDKs for other languages, and option D is a CLI tool, not an SDK.

Answer

OCI JavaScript SDK.

Answer

OCI Python SDK.

Answer

OCI CLI.

Question 8

Which THREE of the following are core LangChain components?

Accepted Answer

Memory. LangChain core components include Models, Prompts, Memory, Agents, Retrievers, Document Loaders, Chains, etc.

Answer

Embeddings

Answer

Tokens

Question 9

An AI developer is building a document Q&A application using LangChain and OCI Generative AI. They need to split large PDF documents into smaller chunks before embedding. Which text splitter should they use to ensure splits respect sentence boundaries while also controlling chunk size?

Accepted Answer

RecursiveCharacterTextSplitter. RecursiveCharacterTextSplitter splits text recursively by separators (like

, 
, period) to keep semantically related text together and respects sentence-like boundaries. TokenTextSplitter splits by tokens without regard for sentence boundaries. PDFLoader and WebBaseLoader are document loaders, not splitters.

Answer

WebBaseLoader

Answer

PDFLoader

Answer

TokenTextSplitter

Question 10

A company is deploying a LangChain agent that uses a custom tool to query an external API. The agent must handle rate limits gracefully. Which approach should the developer implement?

Accepted Answer

Use the Tool's built-in RateLimiter wrapper with a specified max_requests_per_second. LangChain provides a built-in RateLimiter tool wrapper that can be applied to any tool to enforce rate limits. Alternatively, custom retry logic can be added, but using the built-in wrapper is the recommended pattern.

Answer

Increase the tool's timeout to reduce request frequency

Answer

Deploy multiple agent instances to distribute requests

Answer

Ignore rate limits and rely on the LLM to slow down

Question 11

A data scientist is designing a RAG pipeline using LangChain and Oracle AI Vector Search. They want to ensure that the retrieved documents are diverse and not overly similar to each other. Which TWO approaches can achieve this?

Accepted Answer

Set fetch_k larger than k in the retriever's search_kwargs and use MMR. MMR (Maximum Marginal Relevance) is designed to balance relevance and diversity. Setting fetch_k > k in the retriever and using MMR can also improve diversity.

Answer

Use a smaller chunk_size to create more granular chunks

Answer

Use a higher chunk_overlap to increase redundancy

Answer

Use similarity_search with a high k value

Question 12

A developer is using LangChain's LCEL to build a RAG pipeline. They want to add streaming of the final answer to the user. Which LCEL feature enables streaming output from the model?

Accepted Answer

Calling .stream() on the composed chain. The | operator in LCEL composes components but does not inherently stream. .stream() is a method on runnable objects that yields output chunks. .invoke() returns the full output. .batch() processes multiple inputs.

Answer

Setting streaming=True in the model constructor

Answer

Using .invoke() with a callable

Answer

The | operator between components

Question 13

In LangChain, which component is responsible for connecting a language model to a retriever and a prompt template to answer questions based on retrieved documents?

Accepted Answer

RetrievalQA chain. RetrievalQA chain is designed to combine a retriever and an LLM to answer questions based on retrieved documents.

Answer

LLMChain

Answer

SequentialChain

Answer

AgentExecutor

Question 14

A developer is building a RAG pipeline using LangChain and OCI Generative AI. They need to split a large PDF into overlapping chunks for embedding. Which text splitter and parameter settings are MOST appropriate?

Accepted Answer

RecursiveCharacterTextSplitter with chunk_size=1000, chunk_overlap=200. RecursiveCharacterTextSplitter is designed to split text while maintaining paragraphs and sentences. chunk_size=1000 with chunk_overlap=200 is a typical starting point to preserve context across chunks. TokenTextSplitter counts tokens, which is useful for LLM context limits, but here the requirement is about the text splitter for general use; RecursiveCharacterTextSplitter is the standard choice.

Answer

CharacterTextSplitter with chunk_size=500, chunk_overlap=50

Answer

TokenTextSplitter with chunk_size=512, chunk_overlap=0

Answer

MarkdownHeaderTextSplitter with chunk_size=1000, chunk_overlap=200

Question 15

A team wants to deploy a LangChain agent that can perform mathematical calculations, look up current weather, and search the web. Which tools should they include in the agent's toolkit?

Accepted Answer

Calculator tool, weather API tool, and web search tool. An agent needs specific tools for each capability: a calculator tool for math, a weather API tool for weather, and a web search tool for searching. A single LLM tool or retriever cannot replace dedicated tools for these distinct tasks.

Answer

Only a web search tool, because the LLM can handle calculations internally

Answer

Calculator tool and a retriever tool that fetches from a pre-defined knowledge base

Answer

A single LLM tool that can handle all three tasks