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

Question 1

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. Option D is correct because 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 2

A team is using LangChain's LCEL to build a RAG pipeline. They want to add a step that logs the retrieved documents before passing them to the LLM. How can they achieve this while keeping the chain composable?

Accepted Answer

Insert a RunnableLambda that logs the documents and passes them through. Option A is correct because `RunnableLambda` in LangChain's LCEL allows you to inject a custom function that logs the documents and then returns them unchanged, preserving the chain's composability. This pattern keeps the pipeline modular and testable without modifying the retriever or LLM internals.

Answer

Use a callback handler attached to the LLM to capture input documents

Answer

Add a separate logging service that intercepts the chain's output

Answer

Modify the retriever's internal code to add logging

Question 3

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 4

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 5

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 6

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 7

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 is designed to run multiple chains in order, where the output of each chain becomes input to the next. This fits the use case of summarizing then translating.

Answer

RetrievalQA

Answer

LLMChain

Answer

ConversationalRetrievalChain

Question 8

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. OracleVS (Option A) is correct because it is the LangChain vector store integration specifically designed for Oracle Database 23ai, supporting transactional updates (INSERT, UPDATE, DELETE) via the Oracle AI Vector Search feature. This allows the RAG application to maintain consistency with ACID properties when modifying vector embeddings.

Answer

FAISS

Answer

CharacterTextSplitter

Answer

TokenTextSplitter

Question 9

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 10

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 setting 'fetch_k' to a value larger than 'k' allows the retriever to first fetch a larger pool of candidate documents (fetch_k) and then apply a diversity-promoting algorithm like MMR to select the final 'k' documents. This reduces redundancy by ensuring the returned set is not dominated by similar documents from the same region of the vector space.

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 11

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. Documents loaded from PDFs must be split into smaller chunks (using a text splitter) and then embedded into vector representations before they can be indexed in a vector store.

Answer

Translate the documents into English

Answer

Encrypt the documents with AES-256

Answer

Compress the documents with gzip

Question 12

A company is deploying a LangChain application using OCI Generative AI. They need to comply with a policy that requires all prompts sent to the LLM to be logged for audit, and they must also handle rate limits gracefully. Which TWO strategies should they implement?

Accepted Answer

Implement a custom LangChain callback that logs the prompt before sending it to the model. Using LangChain callbacks (e.g., on_llm_start) allows capturing prompts for logging without modifying the chain. For rate limits, adding a retry with exponential backoff (e.g., via tenacity or a custom callback) ensures resilience without dropping requests.

Answer

Use a faster LLM to reduce response time

Answer

Increase the batch size of requests to reduce the number of API calls

Answer

Store the full conversation history in the prompt's system message

Question 13

A developer is using LangChain's ChatPromptTemplate to construct a prompt for a conversational agent. The prompt should include a system message, a placeholder for conversation history, and the latest user query. Which TWO components should they include in the template?

Accepted Answer

SystemMessage. Option B is correct because `SystemMessage` is the LangChain class used to define the system-level instruction that sets the behavior and context for the conversational agent. In a `ChatPromptTemplate`, the system message is typically the first message in the prompt, providing overarching guidance to the model before any conversation history or user input is added.

Answer

AIMessage

Question 14

A developer is using LangChain with Oracle AI Vector Search (OracleVS) to store embeddings. They notice that similarity search queries are slow. Which THREE actions could improve query performance?

Accepted Answer

Use a smaller embedding model (e.g., 384 dimensions instead of 1536). Option C is correct because using a smaller embedding model (e.g., 384 dimensions instead of 1536) reduces the size of each vector stored in Oracle AI Vector Search. This directly decreases memory bandwidth and storage I/O during similarity search, leading to faster distance computations and overall query performance, especially when combined with an index.

Answer

Reduce the chunk_size parameter in the text splitter

Answer

Switch from HNSW to IVF with a low number of centroids

Question 15

A developer wants to load a CSV file containing customer feedback and split it into chunks for a RAG pipeline. Which TWO LangChain components should they use?

Accepted Answer

TokenTextSplitter. Option C (TokenTextSplitter) is correct because it splits text into chunks based on token count, which is essential for RAG pipelines to ensure each chunk fits within the context window of an LLM. Option D (CSVLoader) is correct because it is specifically designed to load CSV files, making it the appropriate choice for ingesting the customer feedback data.

Answer

PDFLoader

Answer

TextLoader