Quiz for LangGraph and Agentic AI module#
No. |
Training Unit |
Lecture |
Training content |
Question |
Level |
Mark |
Answer |
Answer Option A |
Answer Option B |
Answer Option C |
Answer Option D |
|---|---|---|---|---|---|---|---|---|---|---|---|
1 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
LangGraph vs LangChain |
What is the primary structural difference between LangChain chains and LangGraph? |
Easy |
1 |
B |
LangGraph only supports linear flows. |
LangGraph supports cyclic flows and loops, while chains are typically linear. |
LangGraph removes the need for LLMs. |
LangChain supports state persistence, while LangGraph does not. |
2 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State Management |
What is the recommended pattern for State management in LangGraph? |
Easy |
1 |
A |
Messages-centric pattern (using |
Context-only pattern (using distinct string fields). |
Stateless execution. |
Database-only pattern. |
3 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State Reducers |
What is the specific function of |
Medium |
1 |
C |
It deletes old messages to save memory. |
It converts messages to JSON strings. |
It appends new messages to the list and handles deduplication/merging. |
It sends messages directly to the LLM without storing them. |
4 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Message Types |
Which message type represents a response generated by the Large Language Model in LangChain? |
Easy |
1 |
B |
HumanMessage |
AIMessage |
SystemMessage |
ToolMessage |
5 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Nodes |
In a LangGraph node function, what is the standard return value format? |
Medium |
1 |
D |
A string containing the answer content. |
A boolean indicating if the node succeeded. |
The full State object with all fields updated. |
A dictionary representing State updates (deltas). |
6 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Edges |
What is the primary purpose of “Conditional Edges” in LangGraph? |
Medium |
1 |
A |
To route execution dynamically based on the current state (e.g., router logic). |
To connect nodes that always run in a fixed sequence. |
To visualize the graph structure in a user interface. |
To provide metadata about the connection type. |
7 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Context |
Which of the following is typically considered “Context” (metadata) rather than part of the “Messages” list in the State? |
Easy |
1 |
C |
The latest query from the user. |
The LLM’s suggested response. |
|
The result returned from a tool execution. |
8 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Graph API |
Which class is used to initialize a stateful graph workflow with a specific schema? |
Easy |
1 |
B |
|
|
|
|
9 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Nodes |
What does a Node typically do in a LangGraph execution step? |
Easy |
1 |
A |
Reads the current state, processes it (LLM/tools), and returns an update. |
Only routes traffic between other nodes without processing. |
Permanently saves all session data to a physical database. |
Renders the final output for the user. |
10 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
Graph API |
What does the |
Medium |
1 |
C |
A static JSON representation of the graph. |
A standard Python dictionary. |
A |
A direct connection to the underlying database. |
11 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
What is the core role of the messages field in a LangGraph State? |
Easy |
1 |
A |
It is the core communication channel for I/O. |
It stores API keys. |
It defines the database schema. |
It acts as a hardcoded counter. |
12 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
When should you use context fields instead of the messages list? |
Medium |
1 |
C |
To track conversation history. |
To store the compiled graph. |
For configuration, metadata, and tracking. |
To entirely replace the LLM. |
13 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
What is a primary characteristic of the built-in MemorySaver checkpointer? |
Easy |
1 |
B |
It encrypts data to the cloud. |
It stores data in-memory; lost on restart. |
It requires PostgreSQL. |
It is the standard for production. |
14 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
What attribute of the last message is checked to trigger tools? |
Medium |
1 |
D |
status_code |
system_prompt |
error_log |
tool_calls |
15 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
What are the standard sequential steps a node function follows? |
Medium |
1 |
A |
Read messages, process, return deltas. |
Render UI and capture clicks. |
Connect to DB and drop tables. |
Initialize checkpointer. |
16 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
How can agents identify their outputs in a shared messages list? |
Hard |
1 |
B |
Appending their name to content. |
Tagging the AIMessage with a name attribute. |
Using separate state dicts. |
Converting messages to numbers. |
17 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
Why is a checkpointer essential for a stateful LangGraph application? |
Medium |
1 |
C |
It prevents hallucinations. |
It translates output. |
It allows state persistence and resumption. |
It converts Python to JSON. |
18 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
What behavior does add_messages provide? |
Hard |
1 |
D |
Deletes all old messages. |
Translates messages. |
Limits the array to 10 messages. |
Appends new messages and handles deduplication. |
19 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
How can a developer visually inspect the structure of a compiled LangGraph app? |
Easy |
1 |
A |
Using the draw_mermaid_png() method. |
Printing the raw compile() output. |
Reading system log files. |
Checking the SQLite database. |
20 |
Unit 1: LangGraph Foundations & State Management |
Lec1 |
State |
Which method determines the first node that executes in a StateGraph? |
Easy |
1 |
C |
start_node() |
init_graph() |
set_entry_point() |
add_first_edge() |