What is the language used to create and design web pages? Hypertext Markup Language (HTML) JavaScript Python Cascading Style Sheets (CSS)

Hypertext Markup Language (HTML)

A sales manager wants to improve their processes using AI in Salesforce. Which application of AI would be most beneficial? Inventory management Automated email marketing Chatbots for customer service Lead scoring and opportunity forecasting

Lead scoring and opportunity forecasting

What is the most likely impact that high-quality data will have on customer relationships? Higher data storage expenses Increased marketing costs Improved customer trust and satisfaction Reduced customer engagement

Improved customer trust and satisfaction

What are the key components of the data quality standard? Accuracy, Completeness, Consistency Speed, Efficiency, Cost Innovation, Creativity, Flexibility Security, Privacy, Compliance

Accuracy, Completeness, Consistency

What is the role of Salesforce's Trusted AI Principles in the context of CRM systems? Reducing operational costs Enhancing user interface design Increasing sales through automation Guiding ethical and responsible use of AI

Guiding ethical and responsible use of AI

A business analyst wants to improve business by enhancing their sales processes and customer support. Which AI applications should they use to meet their needs? Automated billing, inventory tracking, and payroll management Data encryption, firewall protection, and network monitoring Social media monitoring, email marketing, and SEO optimization Lead scoring, opportunity forecasting, and case classification

Lead scoring, opportunity forecasting, and case classification

Why is it critical to consider privacy concerns when dealing with AI and CRM data? Reduces system efficiency Ensures compliance with laws and regulations Enhances user interface design Increases data storage costs

Ensures compliance with laws and regulations

What is regression in the context of machine learning? An unsupervised learning technique for clustering data A technique for reducing the dimensionality of data A method for classifying data into discrete categories A supervised learning technique used to predict a continuous outcome variable based on input variables

A supervised learning technique used to predict a continuous outcome variable based on input variables

What's the main goal when performing regression? To reduce the number of features in a dataset To cluster data points into groups To classify data into different categories To build a prediction model that can estimate the Y value

To build a prediction model that can estimate the Y value

In regression, what do X and Y represent? X and Y both represent dependent variables X represents independent variables, and Y represents the dependent variable X and Y both represent independent variables X represents the dependent variable, and Y represents independent variables

X represents independent variables, and Y represents the dependent variable

What library is used for linear regression in the course? Scikit-learn's LinearRegression model TensorFlow's LinearModel Keras's LinearLayer PyTorch's LinearRegression

Scikit-learn's LinearRegression model

What are the basic steps in building a regression model in Python? Define problem, gather data, preprocess data, train model, test model Collect data, clean data, visualize data, train model, evaluate model Generate synthetic data, pick a model, set hyperparameters, prepare data, fit the model, predict Identify features, select model, train model, validate model, deploy model

Generate synthetic data, pick a model, set hyperparameters, prepare data, fit the model, predict

How do you interpret the regression output (coefficients and intercept)? The coefficient is the average change in Y; The intercept is the median of Y The coefficient represents how much Y changes with a one-unit increase in X; The intercept is the Y value when all X values are 0 The coefficient is the product of X and Y; The intercept is the difference between X and Y The coefficient is the sum of all X values; The intercept is the sum of all Y values

The coefficient represents how much Y changes with a one-unit increase in X; The intercept is the Y value when all X values are 0

What is the predicted value in regression called? Y, representing the actual output β, representing the regression coefficient X, representing the input variable Ŷ (Y hat), representing the model's predicted output

Ŷ (Y hat), representing the model's predicted output

How do you evaluate the accuracy of your regression model? By using metrics like mean squared error (MSE) - the lower the MSE, the better the model's predictions By calculating the sum of squared errors (SSE) By using the confusion matrix By measuring the correlation coefficient

By using metrics like mean squared error (MSE) - the lower the MSE, the better the model's predictions

What's a good next step after simple regression? Try regression with a real-world dataset and multiple features to build a more complex model Implement clustering algorithms Switch to classification tasks Focus on data preprocessing techniques

Try regression with a real-world dataset and multiple features to build a more complex model

What is a decision tree in machine learning? A clustering algorithm that groups data points A neural network with multiple layers A linear model that predicts continuous outcomes A model that splits data into branches based on decision rules

A model that splits data into branches based on decision rules

What is AI and how does it perform tasks that usually require human intelligence? AI is a type of software that only performs calculations. AI is when machines perform tasks that usually require human intelligence. AI is a hardware component that enhances computer speed. AI is a network protocol for data transfer.

AI is when machines perform tasks that usually require human intelligence.

What is the difference between narrow AI and general AI? Narrow AI is for a specific task, general AI is humanlike intelligence. Narrow AI is used for gaming, general AI is used for robotics. Narrow AI and general AI are the same. Narrow AI is humanlike intelligence, general AI is for specific tasks.

Narrow AI is for a specific task, general AI is humanlike intelligence.

Which of the following are examples of AI applications? Database management systems, file storage, network protocols. Word processors, spreadsheets, presentation software. Operating systems, web browsers, email clients. Chatbots, voice assistants, recommendation systems.

Chatbots, voice assistants, recommendation systems.

What are the basics of machine learning and how does it learn patterns from data? AI relies on human input for every decision. AI uses predefined rules to make decisions. AI learns patterns from data (training data). AI does not require any data to function.

AI learns patterns from data (training data).

What are the basic steps involved in machine learning? Input → Random Guessing → Output. Data Collection → Manual Processing → Output. Data Analysis → Human Intervention → Output. Input → Pattern Recognition → Output.

Input → Pattern Recognition → Output.

How does image recognition and computer vision work in AI? AI uses human-like vision to interpret images. AI cannot process images at all. AI sees using pixels and patterns, not context like humans. AI relies on manual input to recognize images.

AI sees using pixels and patterns, not context like humans.

What is Natural Language Processing (NLP) in the context of AI? AI translating binary code to human language. AI understanding and using human language. AI developing new dialects for communication. AI creating new programming languages.

AI understanding and using human language.

Which of the following are examples of Natural Language Processing applications? Spreadsheet applications, database management. Video editing software, graphic design tools. Web browsers, email clients, file explorers. Siri, translation apps, ChatGPT.

Siri, translation apps, ChatGPT.

Why can Natural Language Processing be difficult for AI systems? AI systems have perfect language understanding. Language processing is easy due to uniformity. Slang, accents, multiple meanings, and bias in language understanding. AI systems do not process language at all.

Slang, accents, multiple meanings, and bias in language understanding.

How does bias occur in relation to AI systems? When training data is unfair or unbalanced. Bias is a result of AI systems being too simple. Bias occurs when AI systems are too advanced. Bias is inherent in all AI systems.

When training data is unfair or unbalanced.

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