Selling HPE Solution Foundations - Module 1

As of the text's context, how can the ubiquity of AI in modern times be described?

• Limited to specific sectors
• On the decline
• Exclusive to tech companies
• Everywhere
• Not trusted by the public

Before the advent of DALL-E 2 and ChatGPT, in which of the following applications was AI most commonly experienced by most people?

• Virtual reality
• Text editors and autocorrect capabilities
• Self-driving cars
• Robotics
• Gaming AI

According to Antonio Neri, what enables AI to accelerate dramatically?

• Limiting its use to processing a small subset of data
• The ability to process large amounts of data
• Focusing solely on hardware improvements
• Reducing the need for human interaction
• Ignoring software advancements

What does Antonio Neri describe as the "true power of AI"?

• Its capacity for entertainment
• Its ability to replace human jobs
• Its potential for military applications
• Its ability to turn questions into discovery and insight into action
• Its use in social media algorithms

How does AI primarily function in relation to human intelligence?

• It disregards human intelligence concepts
• It mimics human intelligence to process data
• It focuses solely on manual tasks without data processing
• It replaces human emotions with algorithms
• It enhances human intelligence without data processing

How are ML models typically developed for tasks like image recognition or spam detection?

• By explicitly programming every possible outcome
• Through a one-time setup without the need for further input
• Developers build and train the model with a large amount of data
• Using minimal data for quick and general solutions
• By outsourcing the task to external intelligence

Which phrase best encapsulates the importance of quality data in machine learning?

• "Quality over quantity"
• "Less is more"
• "Garbage in, garbage out"
• "First impressions matter"
• "Beauty is in the eye of the beholder"

How do developers ensure that a machine learning model is ready for real-world deployment?

• By deploying it immediately after the model is built
• By monitoring the application before training
• By training the model once with a fixed set of data
• By validating the model with new data to decide which performs best
• By choosing the most complex algorithm available

After gathering and selecting quality data, what is the next step in developing an ML application?

• Deploying the model
• Selecting an algorithm and building the model
• Monitoring the application
• Validating the model with new data
• Promoting the application to potential users

Can you identify an example of a deep learning application?

• A basic calculator
• A self-driving car
• A digital watch
• A spreadsheet software
• An email client