1Z0-1127-25 LangChain and AI Application Development Practice Test 2 — 15 Questions

Question 1

An organization using Oracle AI Vector Search for a RAG application wants to minimize latency for vector similarity searches on a dataset of 10 million vectors. Which index type and parameter combination is MOST likely to achieve the lowest latency while maintaining high recall?

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

Accepted Answer

HNSW with neighbors=64. HNSW (Hierarchical Navigable Small World) graphs are designed for low-latency approximate nearest neighbor (ANN) search, and increasing the number of neighbors (efConstruction/efSearch) directly improves recall at the cost of memory. With 10 million vectors, HNSW with neighbors=64 provides a denser graph, reducing the number of hops during search and achieving lower latency than IVF-based methods, which require scanning multiple clusters. This combination offers the best trade-off for high recall and minimal latency in a RAG application.

Answer

IVF with quantization and probes=5

Answer

HNSW with neighbors=32

Answer

IVF with probes=10

Question 2

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?

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 3

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 4

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. `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 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 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 [CORRECT]. 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 [wrong]

Answer

CharacterTextSplitter [wrong]

Answer

TokenTextSplitter [wrong]

Question 8

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'. 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 9

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). To improve similarity search performance in Oracle AI Vector Search (OracleVS), two approaches are effective. Option C reduces the embedding dimensionality (e.g., from 1536 to 384), which decreases the size of each vector and speeds up distance computations. Option D creates an HNSW vector index, which organizes vectors in a hierarchical graph to enable faster approximate nearest neighbor search. While option E (increasing efSearch) can improve recall, it typically increases query latency because the search explores more candidates, so it does not improve query performance in terms of speed. Options A and B are incorrect: reducing chunk size increases the number of vectors and may slow down search, and switching to IVF with too few centroids makes clusters large, causing search to degrade to near brute-force.

Answer

Reduce the chunk_size parameter in the text splitter

Answer

Switch from HNSW to IVF with a low number of centroids

Answer

Increase the efSearch parameter in the HNSW index

Question 10

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. (TokenTextSplitter) is correct because it splits text into chunks based on token count, ensuring each chunk fits within the LLM's context window—a key requirement for RAG pipelines. 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. Option E (TextSplitter) is incorrect because it is an abstract base class and not a directly usable component; developers must use a concrete implementation like TokenTextSplitter.

Answer

PDFLoader

Answer

TextLoader

Answer

TextSplitter

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. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store at query time, without requiring model retraining. When documents are updated monthly, only the vector store needs to be re-indexed, while the underlying LLM remains unchanged, making it cost-effective and scalable.

Answer

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

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

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

Question 12

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 13

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 14

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 15

Which LangChain memory type stores the entire conversation history as a list of messages and is best for simple, short conversations?

Accepted Answer

ConversationBufferMemory. ConversationBufferMemory stores the full history, making it suitable for short dialogues but not for long ones due to token limits.

Answer

ConversationTokenBufferMemory

Answer

ConversationSummaryMemory

Answer

ConversationBufferWindowMemory