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AI Challenges and Solutions in 2025

Total questions: 20

Worksheet time: 10mins

Name
Class
Date
1.

What is the biggest challenge AI stacks face in 2025, according to the article?

a)

Lack of data

b)

Poor model performance

c)

Disconnected tools and systems

d)

Limited cloud storage

2.

What percentage of companies say their AI investments aren’t delivering expected results in 2025?

a)

25%

b)

45%

c)

Over 68%

d)

90%

3.

What does MCP stand for?

a)

Model Context Protocol

b)

Modular Connection Pipeline

c)

Machine Control Program

d)

Model Coordination Platform

4.

What is the primary function of MCP?

a)

Train AI models

b)

Optimize neural networks

c)

Connect and coordinate AI systems

d)

Manage cloud infrastructure

5.

MCP acts like which common analogy?

a)

Remote control for AI

b)

Diplomatic translator for AI systems

c)

Firewall for data security

d)

Compiler for AI code

6.

What does MCP replace to simplify system integration?

a)

Training datasets

b)

Hardware accelerators

c)

Fragile APIs and brittle scripts

d)

UI components

7.

Which of the following is NOT a core feature of MCP?

a)

Smart tool discovery

b)

Automatic model training

c)

Persistent context sharing

d)

Common communication protocol

8.

What is 'MCP Host'?

a)

The protocol itself

b)

An AI assistant or app using MCP

c)

A database connector

d)

An external API

9.

What role does the 'MCP Client' play?

a)

Executes AI queries

b)

Forwards requests from host to server

c)

Stores contextual data

d)

Generates reports

10.

What is the 'MCP Server' responsible for?

a)

Training AI agents

b)

Wrapping tools, databases, or APIs for access

c)

Filtering input data

d)

Encrypting data logs

11.

MCP allows AI systems to collaborate without:

a)

Constant human oversight

b)

Any training

c)

GPU acceleration

d)

Cloud dependency

12.

Which of the following is a benefit of MCP?

a)

Requires frequent manual configuration

b)

Enables real-time tool discovery and integration

c)

Only works with OpenAI tools

d)

Prevents data sharing

13.

What happens when a new tool is added to an MCP-enabled stack?

a)

Manual code must be added

b)

The AI system can use it immediately

c)

Old tools stop working

d)

System must reboot

14.

Which AI platforms are starting to support MCP?

a)

Only ChatGPT

b)

None yet

c)

Claude, ChatGPT, Replit, Cursor

d)

Only enterprise-only platforms

15.

MCP helps teams avoid:

a)

Working with AI at all

b)

Vendor lock-in and spaghetti stacks

c)

Using open-source tools

d)

Model fine-tuning

16.

What is one real-world use case given for MCP in the article?

a)

Coordinating tools like Zendesk, Salesforce, and Google Sheets for research

b)

Powering gaming engines

c)

Running security cameras

d)

Developing AR/VR apps

17.

How does MCP reduce development time?

a)

Uses faster GPUs

b)

Reduces training epochs

c)

Automates integration across tools

d)

Converts code to Python

18.

MCP is compared to which common technology interface?

a)

HDMI

b)

USB-C

c)

Ethernet

d)

Bluetooth

19.

What makes MCP scalable?

a)

Modular design and plug-in architecture

b)

Centralized database system

c)

Requires less memory

d)

Built-in cloud hosting

20.

What kind of roadmap is mentioned at the end of the article?

a)

AI Ethics Roadmap

b)

4-Phase MCP Integration Roadmap

c)

Neural Net Deployment Plan

d)

API Optimization Guide