1Z0-1127-25 LangChain and AI Application Development Practice Test 3 — 15 Questions

Question 1

A developer wants to index a large corpus of HTML web pages for a RAG pipeline using LangChain. They need to load the content from URLs, split the text into chunks, and generate embeddings. Which combination of LangChain components should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

WebBaseLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings. WebBaseLoader loads HTML content, RecursiveCharacterTextSplitter splits it, and OCIGenAIEmbeddings generates embeddings.

Answer

PDFLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

Answer

CSVLoader, RecursiveCharacterTextSplitter, OCIGenAIEmbeddings

Answer

WebBaseLoader, TokenTextSplitter, OCIGenAI

Question 2

In LangChain, what is the purpose of the LCEL (LangChain Expression Language) | operator?

Accepted Answer

To pipe the output of one component into the next component. The | operator in LCEL is used to compose chains by passing the output of one component as input to the next.

Answer

To run two chains in parallel

Answer

To concatenate two prompt templates

Answer

To compare outputs of two different models

Question 3

A team is building a conversational chatbot using LangChain and OCI Generative AI. They want to maintain a summary of the conversation rather than storing the entire history, to keep within token limits. Which memory class should they use, and what additional step is required when initializing the memory?

Accepted Answer

ConversationSummaryMemory; provide an LLM to generate summaries. ConversationSummaryMemory is designed to maintain a running summary of the conversation instead of storing the full history, which directly addresses the requirement to stay within token limits. The additional step required is providing an LLM (e.g., via `llm=ChatOpenAI(...)`) because the memory class uses the LLM to generate and update the summary dynamically.

Answer

ConversationBufferWindowMemory; set a window size

Answer

ConversationTokenBufferMemory; set a token limit

Answer

ConversationBufferMemory; no additional step

Question 4

An application uses LangChain's ConversationalRetrievalChain with memory. Users report that the chatbot occasionally repeats information from earlier in the conversation even when the new question is unrelated. What is the most likely cause?

Accepted Answer

The retriever is returning irrelevant documents from the conversation history. The chain retrieves documents based on the conversation history plus the current question; if the retriever returns irrelevant chunks from past context, the LLM may repeat old information.

Answer

The memory type is set to ConversationSummaryMemory

Answer

The chunk_size is too small, causing loss of context

Answer

The LLM temperature is set too high

Question 5

Which Oracle AI Vector Search index type is designed for approximate nearest neighbor search and uses a navigable small world graph?

Accepted Answer

HNSW. HNSW (Hierarchical Navigable Small World) is a graph-based index for ANN search.

Answer

VECTOR

Answer

IVF

Answer

BTREE

Question 6

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 7

A company has a collection of PDF documents that are 500 pages each. They want to build a RAG system using LangChain and FAISS. They need to ensure that each chunk has enough context for accurate retrieval while keeping chunk size small enough for efficient embedding. They also want some overlap between chunks to avoid losing context at boundaries. Which text splitter configuration is most appropriate?

Accepted Answer

RecursiveCharacterTextSplitter with chunk_size=1000 and chunk_overlap=200. RecursiveCharacterTextSplitter with a moderate chunk_size (e.g., 1000 characters) and a chunk_overlap of 10-20% is a standard choice for balancing context and efficiency.

Answer

RecursiveCharacterTextSplitter with chunk_size=5000 and chunk_overlap=0

Answer

CharacterTextSplitter with chunk_size=2000 and chunk_overlap=500

Answer

TokenTextSplitter with chunk_size=100 and chunk_overlap=10

Question 8

A developer is building a LangChain application that uses OCI Generative AI service. They want to implement streaming responses from the LLM to improve user experience. Which TWO actions are necessary to enable streaming?

Accepted Answer

Use a StreamingStdOutCallbackHandler or custom callback to handle stream events. LangChain's streaming architecture requires a callback handler (like `StreamingStdOutCallbackHandler` or a custom one) to process tokens as they are emitted from the LLM. Without a callback, the stream events are generated but not consumed, so the application cannot output partial results in real time.

Answer

Increase the chunk_size to speed up streaming

Answer

Disable memory to avoid buffering the stream

Answer

Use async LangChain syntax for streaming

Question 9

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 10

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 11

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 12

Which LangChain abstraction is responsible for storing and retrieving conversation history to maintain context across multiple turns in a chatbot?

Accepted Answer

Memory. Memory in LangChain is designed to store and retrieve conversation history, enabling context-aware responses. Models generate text, chains orchestrate steps, and agents decide actions — none handle history directly.

Answer

Agent

Answer

Chain

Answer

Model

Question 13

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 14

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 15

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