1Z0-1127-25 LangChain and AI Application Development Practice Test 6 — 15 Questions

Question 1

A developer wants to ensure that a LangChain RetrievalQA chain returns diverse results, avoiding near-duplicate passages in the retrieved context. Which retrieval parameter should be configured on the retriever?

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

Accepted Answer

Maximum Marginal Relevance (MMR). Maximum Marginal Relevance (MMR) is specifically designed to reduce redundancy by balancing relevance with diversity. When configured on a retriever, MMR selects documents that are both relevant to the query and dissimilar to each other, thus avoiding near-duplicate passages in the retrieved context.

Answer

top_k

Answer

similarity_search

Answer

chunk_overlap

Question 2

An application uses Oracle AI Vector Search with a VECTOR data type column. The team wants to perform a similarity search to find the top 5 most similar vectors to a query vector. Which SQL clause is necessary to achieve this?

Accepted Answer

ORDER BY VECTOR_DISTANCE(embedding, :query_vec) FETCH FIRST 5 ROWS ONLY. Oracle AI Vector Search uses the VECTOR_DISTANCE function to compute the distance between vectors, and the FETCH FIRST 5 ROWS ONLY clause limits the result set to the top 5 most similar vectors. This combination performs an exact nearest neighbor search, which is the standard SQL approach for similarity queries on a VECTOR column.

Answer

WHERE embedding SIMILAR TO :query_vec TOP 5

Answer

ORDER BY VECTOR_COMPARE(embedding, :query_vec) LIMIT 5

Answer

WHERE KNN(embedding, :query_vec, 5)

Question 3

A developer is building a conversational AI application using LangChain and needs to persist chat history across sessions. Which TWO approaches can they use? (Choose TWO.)

Accepted Answer

Use ConversationSummaryMemory and store the summary in a file. ConversationSummaryMemory can be persisted by storing its summary in a file, which allows chat history to survive across sessions. Option E is correct because ConversationBufferMemory can be explicitly saved to a database, providing durable storage for the conversation buffer. Both approaches decouple memory from the in-memory lifecycle, enabling cross-session persistence.

Answer

Use the agent's memory parameter with a default in-memory store

Answer

Enable streaming responses to automatically save history

Answer

Use ChatMessageHistory without a backing store

Question 4

A company is deploying a LangChain application on OCI and needs to implement error handling and rate limit management. Which THREE strategies should they consider? (Choose THREE.)

Accepted Answer

Implement retry logic with exponential backoff when receiving 429 (Too Many Requests) responses. A is correct because HTTP 429 (Too Many Requests) responses indicate rate limiting by the API provider. Implementing retry logic with exponential backoff is a standard resilience pattern that progressively increases wait times between retries, preventing further rate limit violations and allowing the system to recover gracefully without overwhelming the endpoint.

Answer

Increase the chunk_size parameter in the text splitter

Answer

Resubscribe to the model endpoint if errors occur

Question 5

You need to build a RAG pipeline using LangChain and OCI Generative AI. The pipeline must load PDF documents, split them into chunks, embed them, store in a vector store, and retrieve relevant chunks at query time. Which THREE components are essential? (Choose THREE.)

Accepted Answer

RecursiveCharacterTextSplitter. A is correct because RecursiveCharacterTextSplitter is the standard text splitter in LangChain for splitting documents into chunks while preserving semantic boundaries (e.g., paragraphs, sentences). It recursively splits on different separators (like newlines, spaces) to maintain context, which is critical for effective retrieval in a RAG pipeline.

Answer

CSVLoader

Answer

Chroma

Question 6

A developer is building a LangChain-powered application that must maintain conversation history across multiple turns. They want to store the chat history in Oracle Database. Which memory type and persistence approach should they use?

Accepted Answer

ConversationBufferMemory with a custom chat message history class backed by Oracle Database. The requirement explicitly states storing chat history in Oracle Database. ConversationBufferMemory stores the full conversation history without summarization or windowing, and by implementing a custom chat message history class backed by Oracle Database, the developer can persist the conversation history directly in Oracle, meeting both the memory type and persistence requirements.

Answer

ConversationSummaryMemory with in-memory storage

Answer

ConversationBufferWindowMemory with Redis persistence

Answer

ConversationKGMemory with file-based storage

Question 7

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

Accepted Answer

Text Splitter. The Text Splitter is the LangChain component whose sole job is to break long documents into smaller, often overlapping chunks before they are embedded and stored. It sits between the Document Loader and the embedding/vector-store step in a RAG pipeline. Splitting with overlap preserves context across chunk boundaries so retrieval returns coherent passages.

Answer

Retriever

Answer

Vector Store

Answer

Document Loader

Question 8

A developer is using Oracle AI Vector Search with LangChain to build a RAG system on top of Oracle Database 23ai. They have created a VECTOR column and built an HNSW index. To improve recall at the cost of some accuracy, which index parameter should they adjust?

Accepted Answer

Increase the 'neighbors' parameter. Increasing the 'neighbors' parameter (M) in an HNSW index expands the number of bidirectional links per node during graph construction. This denser graph provides more alternative paths during search, improving recall because the search is less likely to miss relevant vectors, but it also increases memory usage and can slightly degrade search speed due to more edges to traverse.

Answer

Increase the chunk_overlap in the text splitter

Answer

Decrease the 'efConstruction' parameter

Answer

Switch from HNSW to IVF index type

Question 9

A developer wants to use LangChain to connect to OCI Generative AI Service for text generation. Which LangChain wrapper class should they use for the chat model?

Accepted Answer

ChatOCIGenAI. ChatOCIGenAI is the correct LangChain wrapper class for chat-based interactions with OCI Generative AI Service. LangChain distinguishes between standard LLM wrappers (OCIGenAI) and chat model wrappers (ChatOCIGenAI), where the latter is designed to handle multi-turn conversation history and structured message formats (HumanMessage, AIMessage, SystemMessage). This aligns with the developer's requirement for a chat model.

Answer

OCIGenAI

Answer

ChatOpenAI

Answer

OCIGenAIEmbeddings

Question 10

A company is deploying a LangChain agent for internal data analysis. The agent uses a ReAct pattern with tools including a Python REPL and a SQL query tool. What is the PRIMARY security concern when exposing this agent to non-technical users?

Accepted Answer

Arbitrary code execution via the Python REPL tool. The Python REPL tool allows the agent to execute arbitrary Python code on the host system. When exposed to non-technical users, an attacker can craft prompts that cause the agent to run malicious code, leading to full remote code execution, data exfiltration, or system compromise. This is the most severe risk because it bypasses all data access controls and directly impacts the underlying infrastructure.

Answer

Model hallucination when generating SQL queries

Answer

Prompt injection leading to incorrect SQL queries

Answer

Data leakage through the SQL query tool

Question 11

A developer needs to build a chain that first summarizes a long document, then translates the summary into French. Which LangChain chain type allows executing these steps in sequence with the output of one step feeding into the next?

Accepted Answer

SequentialChain. SequentialChain in LangChain is designed to execute multiple chains in order, passing the output of one step as input to the next. It supports multiple inputs and outputs and is the canonical chain type for multi-step pipelines like summarize-then-translate. LLMChain is a single-step chain, and RetrievalQA/ConversationalRetrievalChain are retrieval-focused, not general sequential pipelines.

Answer

RetrievalQA

Answer

LLMChain

Answer

ConversationalRetrievalChain

Question 12

A developer is building a RAG application with LangChain and Oracle AI Vector Search. They need to choose a text splitter that respects semantic boundaries (e.g., paragraphs, sentences) and a vector store that supports transactional updates in Oracle Database 23ai. Which TWO options should they select?

Accepted Answer

OracleVS. Option E, RecursiveCharacterTextSplitter, is correct because it recursively splits text using an ordered list of separators (defaulting to paragraph breaks, then newlines, spaces, and finally characters), which preserves semantic boundaries like paragraphs and sentences—exactly what the developer needs. Option A, OracleVS, is correct because it is LangChain's vector store integration for Oracle Database 23ai, which supports transactional DML operations (INSERT/UPDATE/DELETE with commit/rollback) on vector data, satisfying the transactional-update requirement. Option B, FAISS, is not appropriate because it is an in-memory similarity-search library without transactional update semantics tied to Oracle Database 23ai. Option C, CharacterTextSplitter, splits on a single fixed separator and does not recursively respect paragraph/sentence structure, so it fails the semantic-boundary requirement. Option D, TokenTextSplitter, splits by token count rather than semantic boundaries, so it also does not meet the requirement.

Answer

FAISS

Answer

CharacterTextSplitter

Answer

TokenTextSplitter

Question 13

A team is building a LangChain agent that needs to answer questions using both a company-internal knowledge base (stored in Oracle AI Vector Search) and live web search. Which THREE components should they include in the agent setup?

Accepted Answer

AgentExecutor to manage the ReAct loop. A LangChain agent requires a set of tools (vector store retriever as a tool, web search tool), and an AgentExecutor to orchestrate the reasoning and tool calls. The LLM is already part of the agent definition.

Answer

ConversationBufferMemory for storing chat history

Answer

A custom tool for logging agent steps

Question 14

A developer is using LangChain's RetrievalQA chain with a vector store. They want to improve the diversity of retrieved documents to avoid redundant information. Which TWO parameters or methods should they adjust?

Accepted Answer

Set the 'fetch_k' parameter to a value larger than 'k'. Option D is correct because in LangChain's vector store retrievers, 'fetch_k' controls how many candidate documents are initially pulled from the vector store before the final selection, so setting 'fetch_k' larger than 'k' gives the retriever a bigger pool to choose from and enables diversity-oriented selection (such as MMR) to actually pick less redundant documents. Option E is correct because Maximum Marginal Relevance (MMR) explicitly re-ranks the fetched candidates by balancing similarity to the query against dissimilarity to already-selected documents, which directly reduces redundant information in the retrieved set. The other options do not belong: increasing 'chunk_size' (A) changes document granularity but not retrieval diversity, setting 'k' very high (B) just returns more documents and can increase redundancy, and swapping the embedding model (C) alters representation quality but does not by itself enforce diversity among retrieved results.

Answer

Increase the 'chunk_size' in the text splitter

Answer

Set the 'k' parameter to a very high number

Answer

Use a different embedding model

Question 15

A developer is building a RAG pipeline with LangChain. They have loaded PDF documents with PDFLoader. Which TWO steps must they perform before indexing the documents into a vector store?

Accepted Answer

Split the documents into chunks using a text splitter. Option B is correct because PDFLoader returns whole Document objects, and a text splitter (e.g., RecursiveCharacterTextSplitter) must break them into smaller chunks so each chunk fits the embedding model's token limit and retrieval returns focused context. Option D is correct because a vector store indexes vectors, not raw text, so each chunk must be passed through an embedding model (e.g., OpenAIEmbeddings) to produce the numeric vectors that are stored and later compared via similarity search. Option A is not required, since RAG pipelines work with documents in any language the embedding model supports. Option C is unnecessary because encryption is a security concern, not a prerequisite for indexing. Option E is wrong because gzip compression is irrelevant to vector indexing and would not produce embeddings.

Answer

Translate the documents into English

Answer

Encrypt the documents with AES-256

Answer

Compress the documents with gzip