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Agents Unleashed: Quiz on LLMs & AI Frontiers

Total questions: 30

Worksheet time: 15mins

Name
Class
Date
1.

What does the acronym LLM stand for in the context of AI?

a)

Linear Logic Model

b)

Large Language Model

c)

Limited Learning Machine

d)

Language Learning Mechanism

2.

Which of the following is NOT a current trend in the development of LLMs?

a)

Multimodal capabilities

b)

On-device deployment

c)

Quantum computing integration

d)

Code generation

3.

What is the main goal of an agentic system in AI?

a)

To translate languages

b)

To perform simple tasks on command

c)

To act autonomously towards a goal

d)

To compress data efficiently

4.

Which of the following is a popular open-source LLM framework?

a)

TensorBoard

b)

LangChain

c)

GitHub Copilot

d)

Visual Studio

5.

In the context of agentic AI systems, what is a “tool use” capability?

a)

The ability to install software

b)

The ability to interact with external APIs or environments

c)

The ability to design neural networks

d)

The ability to repair software bugs

6.

Which recent LLM from OpenAI supports multimodal inputs (text, image, audio)?

a)

GPT-3

b)

ChatGPT

c)

GPT-4-turbo

d)

GPT-Neo

7.

What is a significant challenge with deploying agentic systems?

a)

Too much memory usage

b)

Difficulty in writing Java code

c)

Safety, alignment, and hallucination risks

d)

Lack of documentation

8.

Which of the following best describes "chain-of-thought" prompting in LLMs?

a)

Executing multiple threads in parallel

b)

Encouraging the model to list a series of steps or reasoning

c)

Jumping directly to the answer

d)

Forcing the model to restart its answer

9.

Which architecture underlies most modern LLMs, including GPT and BERT?

a)

Recurrent Neural Network

b)

Convolutional Neural Network

c)

Transformer

d)

GAN (Generative Adversarial Network)

10.

What is an "agent loop" in agentic AI systems?

a)

A loop for debugging AI

b)

A repetitive training cycle

c)

A cycle of observe → reason → act

d)

A recursive error handling mechanism

11.

What is one key trend in the evolution of LLMs?

a)

Smaller datasets

b)

Limited access

c)

On-device fine-tuning

d)

Removal of AI safety layers

12.

Which of these tasks can modern LLMs perform?

a)

Language translation

b)

Code generation

c)

Document summarization

d)

All of the above

13.

What does "multimodal" mean in LLMs?

a)

Supporting multiple users

b)

Understanding images, text, and audio

c)

Running on multiple devices

d)

Generating multiple outputs

14.

Which LLM was announced with a "memory" feature to retain chat history over time?

a)

Claude

b)

ChatGPT

c)

Gemini

d)

BERT

15.

What is fine-tuning in the context of LLMs?

a)

Adding noise to models

b)

Training on new data to specialize

c)

Compressing the model

d)

Testing model speed

16.

What defines an agentic AI system?

a)

Pre-trained for customer support

b)

Rule-based chatbot

c)

Autonomously takes actions toward a goal

d)

Only responds to prompts

17.

Which of these is an example of an LLM agent toolchain?

a)

PyTorch

b)

LangChain

c)

TensorBoard

d)

VS Code

18.

What is a “tool use” capability in agentic systems?

a)

Writing code

b)

Searching the web or invoking APIs

c)

Using keyboard shortcuts

d)

Switching UI themes

19.

Which prompt technique helps LLMs reason step-by-step?

a)

Zero-shot

b)

Few-shot

c)

Chain-of-Thought

d)

Rapid-Fire

20.

Which of the following is a key concern in agentic AI systems?

a)

Slow response time

b)

A circular network structureMemory leaks

c)

Safety and hallucinations

d)

Low internet speed

21.

What is a common method to ensure safety in agentic AI systems?

a)

Conducting thorough testing and validation

b)

Implementing strict access controls

c)

Regular software updates

d)

Using outdated algorithms

22.

Which of the following describes a potential benefit of agentic systems?

a)

Reduced computational power requirements

b)

Enhanced automation of complex tasks

c)

Limited functionality

d)

Increased human oversight

23.

What is a primary focus when developing agentic AI systems?

a)

Minimizing user interaction

b)

Maximizing data storage

c)

Ensuring ethical decision-making

d)

Improving graphical user interfaces

24.

What is a common risk associated with the use of agentic AI systems?

a)

Enhanced user engagement

b)

Data privacy concerns

c)

Increased operational costs

d)

Faster processing speeds

25.

Which of the following is a method to improve the performance of LLMs?

a)

Using outdated algorithms

b)

Limiting input types

c)

Increasing training data diversity

d)

Reducing model size

26.

What is the purpose of reinforcement learning in the context of agentic systems?

a)

To enhance user interface design

b)

To optimize decision-making through feedback

c)

To reduce computational load

d)

To simplify code writing

27.

What is a potential ethical concern when deploying agentic AI systems?

a)

Lower operational costs

b)

Bias in decision-making

c)

Increased efficiency

d)

Improved user satisfaction

28.

How do agentic AI systems typically learn from their environment?

a)

Through supervised learning only

b)

Through unsupervised clustering

c)

By using reinforcement learning techniques

d)

By manual programming

29.

What is a key feature of agentic AI systems that distinguishes them from traditional AI?

a)

Ability to process large datasets

b)

Capability to make autonomous decisions

c)

Reliance on human input for every action

d)

Focus on data analysis

30.

What is alignment in AI?

a)

What is alignment in AI?

b)

Model's behavior aligning with human intent and ethics

c)

Training a smaller model

d)

Data cleanup