NEW
Font size
WorksheetsNextWavwe_Agentic AI MCQs
Total questions: 20
Worksheet time: 7mins
What is the main feature of Agentic AI?
Static rules
Autonomy and action-taking
Only data storage
Manual programming
The key difference between Traditional AI and Agentic AI is:
Agentic AI acts independently
Both are the same
Traditional AI is autonomous
None
The 4 core components of Agentic AI are:
Input, Output, Storage, Cloud
Perception, Reasoning, Planning, Action
Data, Code, Server, Network
None
Reinforcement learning helps Agentic AI in:
Memory storage
Learning from feedback/rewards
Cloud hosting
Data transfer
A smart assistant (Alexa, Siri) is an example of:
Agentic AI
Static AI
Manual systems
Traditional DB
A common challenge in multi-agent systems is:
Coordination
Faster internet
Small data
None
Planning algorithms (A*, Dijkstra) are used for:
Searching and decision-making
File storage
Debugging
Testing
A healthcare use-case of Agentic AI could be:
Automated diagnosis assistant
Video streaming
Gaming console
None
Memory in Agentic AI helps in:
Context awareness
Data loss
Slower decisions
None
Large Language Models (LLMs) are used in Agentic AI for:
Reasoning and language tasks
Hardware repair
Image compression
None
'Human-in-the-loop' means:
Humans guide AI in decisions
AI controls humans
No humans involved
Only robots work
An ethical issue in Agentic AI could be:
Bias in decision-making
Fast deployment
Accurate results
None
The role of Agentic AI in self-healing systems is:
Auto-detect and fix issues
More manual effort
Only reporting
None
Agentic AI in Industry 4.0 is used in:
Smart factories
Manual work
Traditional silos
None
A benefit of multi-agent coordination is:
Collaborative problem solving
More errors
Slower processes
None
A finance use-case of Agentic AI is:
Fraud detection agents
Cooking assistant
Game design
None
Autonomy in AI means:
Independent decision-making
Following only manual orders
No decision
None
A real-world example of Agentic AI is:
Autonomous drones
MS Word
Notepad
Calculator
Combining Agentic AI and DevOps can achieve:
Smarter automation in software delivery
Manual deployments
Slower releases
None
The future potential of Agentic AI lies mostly in:
Automation and decision-making
Manual jobs
Slow systems
Paperwork
