LangChain

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Overview

LangChain is an open-source framework for building applications powered by Large Language Models (LLMs). It provides modular components to integrate models, prompts, tools, retrieval systems, and agents into intelligent AI workflows.

Take this assessment to understand LangChain and identify areas for further learning.

Read the FAQs tab carefully for Instructions before beginning the assessment.

Rewards

NYXPoints are used to generate the Leaderboard (coming soon). They are awarded for achieving a certain score.

200 nyxpoints for a passing score of 80% or more
300 nyxpoints for a perfect score of 100%
Didn’t pass? You still get 30 nyxpoints for attempting the assesment

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There are NO pre-requisites to take this assessment. Take this assessment even if you are completely new to Linux.
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Eligible Rewards

300 NyxCoins*

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Question 1 of 30

1. Question
Which of the following best describes the primary purpose of LangChain?
- To build applications by orchestrating LLMs with external components and workflows
- To train large language models from scratch
- To compress transformer models for edge devices
- To replace vector databases
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Question 2 of 30

2. Question
In the latest LangChain architecture, which package primarily contains the core abstractions such as Runnables, Prompt Templates, and Messages?
- langchain-community
- langgraph
- langsmith
- langchain-core
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Question 3 of 30

3. Question
Which statement about ChatPromptTemplate is correct?
- It only accepts a single string as input.
- It automatically stores conversation history.
- It creates prompts specifically from a sequence of chat messages and roles.
- It is only compatible with OpenAI models.
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Question 4 of 30

4. Question
Which statement best describes the advantage of LCEL (LangChain Expression Language)?
- It only supports sequential execution.
- It replaces prompt templates completely.
- It provides a declarative way to compose Runnables into reusable workflows.
- It is used only for vector database operations.
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Question 5 of 30

5. Question
In an LCEL pipeline, what does the pipe (|) operator primarily represent?
- Passing the output of one Runnable as the input to the next Runnable
- Logical OR between two prompts
- Parallel execution of multiple chains
- Conditional execution based on model confidence
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Question 6 of 30

6. Question
Why are Runnables considered the fundamental building blocks of modern LangChain applications?
- They work only with OpenAI models.
- They automatically fine-tune language models during execution.
- They eliminate the need for prompt engineering.
- Every component that participates in an LCEL pipeline follows a common execution interface.
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Question 7 of 30

7. Question
Which Runnable is most appropriate when you need to execute a custom Python function within an LCEL pipeline?
- RunnableBranch
- RunnableParallel
- RunnableLambda
- RunnableAssign
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Question 8 of 30

8. Question
What is the primary purpose of RunnablePassthrough?
- To pass the original input unchanged to the next stage in the pipeline
- To convert text into embeddings
- To execute multiple Runnables simultaneously
- To cache model responses
Correct

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Question 9 of 30

9. Question
What is the key difference between invoke() and stream()?
- invoke() supports only chat models, while stream() supports all models.
- stream() automatically caches outputs, while invoke() does not.
- invoke() can only process strings, whereas stream() accepts dictionaries.
- invoke() returns the complete output after execution, whereas stream() returns chunks incrementally as they are generated.
Correct

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Question 10 of 30

10. Question
Consider the following LCEL pipeline:

Input | Prompt | Model | Output Parser

What is the primary responsibility of the Output Parser?
- To create embeddings from the generated response.
- To optimize prompt length before model execution.
- To store the response in a vector database.
- To transform the model's raw output into a structured or application-friendly format.
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Question 11 of 30

11. Question
What is the primary responsibility of a Document Loader in a RAG pipeline?
- Load data from various sources into a standardized Document format
- Convert documents into embeddings directly
- Split documents into chunks automatically
- Store documents in a vector database
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Question 12 of 30

12. Question
Why is RecursiveCharacterTextSplitter commonly preferred over a simple character-based splitter?
- It always produces chunks of identical size.
- It only works with PDF documents.
- It recursively attempts to split text using meaningful separators before falling back to smaller ones.
- It automatically removes duplicate chunks.
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Question 13 of 30

13. Question
Which statement best describes an embedding model?
- It generates natural language responses from prompts.
- It converts text into dense numerical vectors that capture semantic meaning.
- It stores documents permanently in memory.
- It performs similarity search directly.
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Question 14 of 30

14. Question
What is the primary advantage of using a vector store in a RAG application?
- It replaces the need for language models.
- It automatically summarizes retrieved documents.
- It enables efficient storage and semantic similarity search over embeddings.
- It eliminates hallucinations completely.
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Question 15 of 30

15. Question
What is the key advantage of a Parent Document Retriever?
- It retrieves only the smallest chunks possible.
- It retrieves relevant small chunks while returning their larger parent documents to preserve context.
- It eliminates the need for embeddings.
- It only works with structured data such as CSV files.
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Question 16 of 30

16. Question
In a Retrieval-Augmented Generation (RAG) workflow, what is the correct sequence?
- Prompt → LLM → Retriever → Vector Store
- Query → LLM → Embedding → Retriever → Output
- Query → Embedding → Retriever → Relevant Context → LLM → Response
- Vector Store → Prompt Template → LLM → Retriever
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Question 17 of 30

17. Question
Why is RAG generally preferred over relying solely on an LLM’s internal knowledge?
- It increases the number of model parameters.
- It allows applications to use external, up-to-date, and domain-specific information during generation.
- It eliminates the need for prompt engineering
- It guarantees factually correct responses in every scenario.
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Question 18 of 30

18. Question
In the latest LangChain framework, what is the primary role of a Tool?
- To store conversation history permanently
- To convert prompts into embeddings
- To fine-tune foundation models
- To enable an agent to perform actions or access external capabilities beyond the LLM's built-in knowledge.
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Question 19 of 30

19. Question
Which statement best describes an Agent in LangChain?
- A component that decides which actions or tools to use based on the user's request
- A predefined sequence of prompts executed in a fixed order
- A vector database optimized for retrieval
- A prompt template containing multiple examples
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Question 20 of 30

20. Question
Why would an AI application use tool calling instead of relying solely on an LLM?
- Tool calling reduces the number of model parameters.
- It eliminates prompt engineering entirely.
- It enables the application to interact with external systems, APIs, databases, or custom functions.
- It automatically improves the model's reasoning ability.
Correct

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Question 21 of 30

21. Question
What is the primary purpose of the Model Context Protocol (MCP)?
- To provide a standardized protocol for connecting AI models with external tools, resources, and prompts
- To replace vector databases with graph databases
- To optimize transformer architectures during training
- To compress embeddings before storage
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Question 22 of 30

22. Question
Which of the following scenarios is the best candidate for Human-in-the-Loop?
- Calculating the sum of two numbers
- Approving a bank fund transfer initiated by an AI agent
- Automatically formatting a paragraph
- Splitting a PDF into chunks
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Question 23 of 30

23. Question
Which statement best describes the purpose of Guardrails in LLM applications?
- They define safety, validation, and behavioral constraints before or after model execution.
- They permanently improve model accuracy through retraining.
- They replace prompt templates entirely.
- They optimize vector database indexing
Correct

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Question 24 of 30

24. Question
What is the primary advantage of a Multi-Agent System over a single-agent architecture?
- It always requires fewer API calls
- It eliminates the need for tools.
- Different agents can specialize in distinct tasks, improving modularity and scalability.
- It guarantees lower token consumption.
Correct

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Question 25 of 30

25. Question
In a Supervisor multi-agent architecture, what is the main responsibility of the supervisor agent?
- Execute every task itself without delegation.
- Route tasks to specialized agents and coordinate the overall workflow.
- Store vector embeddings for all agents.
- Replace the need for a state object.
Correct

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Question 26 of 30

26. Question
Which statement best describes State in LangGraph?
- It is a permanent database used to store embeddings.
- It stores only the user's prompt.
- It replaces Prompt Templates.
- It represents the shared data that flows and evolves throughout graph execution.
Correct

Incorrect
Question 27 of 30

27. Question
What is the primary purpose of a Checkpointer in LangGraph?
- To compress prompts before sending them to the LLM.
- To optimize vector similarity search.
- To persist workflow state so execution can resume from previous checkpoints.
- To fine-tune the language model automatically.
Correct

Incorrect
Question 28 of 30

28. Question
A customer support AI uses separate agents for billing, technical issues, and refunds, while another agent decides which specialist should handle each request. Which architecture best matches this design?
- Retrieval-Augmented Generation (RAG)
- RunnableParallel Pipeline
- Contextual Compression Workflow
- Supervisor Multi-Agent Architecture
Correct

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Question 29 of 30

29. Question
Which design principle generally leads to more maintainable AI agent systems?
- One agent should perform every possible task.
- Every agent should directly communicate with every other agent.
- Each agent should have a clear, specialized responsibility with well-defined boundaries.
- Avoid using tools whenever possible.
Correct

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Question 30 of 30

30. Question
Which statement best summarizes the modern LangChain ecosystem?
- LangChain provides composable components for building AI applications, while LangGraph extends it for stateful and agentic workflows.
- LangChain focuses only on prompt engineering for LLMs.
- LangGraph has completely replaced LangChain.
- LangChain applications cannot integrate external tools or retrieval systems.
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LangChain

Eligible Rewards

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