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 3: Tool Calling & Tavily Search	Lec3	Tool Calling	What is the core definition of “Tool Calling” in LLMs?	Easy	1	D	The user clicking a button to run a script.	The LLM memorizing the tool’s source code.	A post-processing step using regular expressions.	The LLM’s ability to decide when to call a tool and suggest structured parameters.
2	Unit 3: Tool Calling & Tavily Search	Lec3	Decorators	Which decorator is used to convert a standard Python function into a LangChain-compatible Tool?	Easy	1	A	@tool	@function	@agent	@node
3	Unit 3: Tool Calling & Tavily Search	Lec3	Tavily Search	What makes Tavily Search API specifically better for RAG applications than standard search engines?	Easy	1	C	It is free for all users without limits.	It performs image and video recognition.	It is optimized for LLMs, returning cleaned and relevant context for grounding.	It searches offline databases exclusively.
4	Unit 3: Tool Calling & Tavily Search	Lec3	Tavily Search	Which parameter in the Tavily Search tool allows getting a direct, synthesized AI answer?	Medium	1	B	search_depth="advanced"	include_answer=True	include_raw_content=True	max_results=1
5	Unit 3: Tool Calling & Tavily Search	Lec3	Tool Design	Why is providing a high-quality docstring/description for a tool critical?	Medium	1	A	The LLM uses this text to understand when and how to select the tool.	It is required for the Python interpreter to run the code.	it is used for automatic type checking.	It encrypts the parameters passed to the tool.
6	Unit 3: Tool Calling & Tavily Search	Lec3	Pydantic	What is the args_schema parameter used for when defining a Custom Tool?	Hard	1	D	To define the tool’s output string format.	To set the unique identifier for the tool.	To configure the API endpoints.	To define and validate the input parameter structure using Pydantic.
7	Unit 3: Tool Calling & Tavily Search	Lec3	LLM Binding	What is the effect of the llm.bind_tools([...]) method call?	Medium	1	B	It executes all provided tools immediately.	It attaches tool schemas to the LLM so the model is aware of their capabilities.	It hardcodes the tool expected outcome into the model’s weights.	It prevents the LLM from ever suggesting a tool call.
8	Unit 3: Tool Calling & Tavily Search	Lec3	Execution	In a standard tool execution flow, who is responsible for the actual execution of the tool logic?	Medium	1	C	The LLM itself (internally).	The external LLM API (e.g., OpenAI servers).	The application runtime environment (e.g., the ToolNode in Python).	The human user via a terminal.
9	Unit 3: Tool Calling & Tavily Search	Lec3	Chaining	What is “Tool Chaining” in an agentic workflow?	Medium	1	A	Using the output of one tool as the input for another tool call.	Running multiple tools in parallel on different servers.	linking the tool’s source code to a git repository.	Reusing the same tool multiple times for the same query.
10	Unit 3: Tool Calling & Tavily Search	Lec3	Data Flow	How does the LLM receive the result of a tool execution back into its context?	Medium	1	D	It predicts the result based on history.	Via a direct callback to its weights.	It does not receive the result; it only knows the tool ran.	As a ToolMessage appended to the conversation state.
11	Unit 3: Tool Calling & Tavily Search	Lec3	Tool Calling	How does Tool Calling interact with systems?	Medium	1	C	Bash scripts.	HTML buttons.	Returning structured JSON invocations.	Downloading source code.
12	Unit 3: Tool Calling & Tavily Search	Lec3	Tool Calling	What terminology do LangChain and Anthropic use?	Easy	1	D	Function Extraction	Action Prompting	Tool Scripting	Tool Use
13	Unit 3: Tool Calling & Tavily Search	Lec3	Tool Design	In OpenAI API, what describes a function’s capabilities?	Medium	1	A	A JSON schema.	A raw Python function.	A Markdown list.	A binary file.
14	Unit 3: Tool Calling & Tavily Search	Lec3	Decorators	What is the purpose of the @tool docstring?	Medium	1	B	Python compilation.	Description used by LLM.	Generating unit tests.	Data encryption.
15	Unit 3: Tool Calling & Tavily Search	Lec3	Tavily Search	What feature makes Tavily suited for AI?	Easy	1	C	Offline support.	Wikipedia-only search.	AI-optimized clean results.	Unlimited free tier.
16	Unit 3: Tool Calling & Tavily Search	Lec3	Tavily Search	Which parameter restricts Tavily to specific domains?	Easy	1	D	restrict_urls	domain_filter	only_sites	include_domains
17	Unit 3: Tool Calling & Tavily Search	Lec3	Chaining	What does “Tool Chaining” allow?	Medium	1	A	Multi-step workflows.	Blockchain security.	Single-tool limits.	Storing tools in a class.
18	Unit 3: Tool Calling & Tavily Search	Lec3	Execution	How are infinite tool hangs handled?	Hard	1	B	Restarting the server.	Async execution with timeout.	Writing faster prompts.	Using @cache.
19	Unit 3: Tool Calling & Tavily Search	Lec3	Tool Design	Which class is extended for custom tools in LangChain?	Hard	1	C	BaseTool	ToolNode	BaseModel (Pydantic).	TypedDict
20	Unit 3: Tool Calling & Tavily Search	Lec3	Execution	What is the best practice for API key management?	Easy	1	D	Hardcoding.	Storing in public Git.	URL parameters.	Using environment variables.