AI

Question 1

What are the Salesforce AI Trusted Principles

Answer 1

Responsibility, Account, Transparency, Empowerment, and Inclusion

Question 2

Responsible:

Answer 2

Safeguarding data that is trusted upon.

Question 3

Accountability

Answer 3

Seeking and leveraging feedback for continuous improvement.

Question 4

Transparency

Answer 4

Developing transparent user experiences and guiding users through machine-driven recommendations.

Question 5

Empowerment

Answer 5

Promoting economic growth and employment opportunities for employees and customers.

Question 6

Inclusion

Answer 6

Respecting societal values for all those affected, not just those who created the AI model.

Question 7

AI Maturity Model

Answer 7

-Ad Hoc
-Organized and Repeatable
-Managed and Sustainable
-Optimized and Innovated

Question 8

Ad hoc

Answer 8

No proper team or resources; initial stage.
Review and risk assessment take place; informal advocacy.

Question 9

Organized and Repeatable

Answer 9

-Executives’ buy-in established.
-Ethical principles are established.
-Building a team of diverse experts.
-Company-wide education.

Question 10

Managed and Sustainable

Answer 10

-Ethical standards integrated from project inception throughout the life cycle.
-Bias mitigation in build or buy decisions.
-Metrics identified to track progress.

Question 11

Optimized and Innovation

Answer 11

-End-to-end inclusive design practices.

-Ethical features and resolving ethical debt are formal parts of the roadmap and resources.

Question 12

Demographic Data:

Answer 12

Statistical data collected for populations (age, gender, race)

Question 13

Baisis in Ai

Answer 13

When AI systems produce systematically prejudiced results due to errors in the machine learning process

Question 14

Data Bias

Answer 14

Occurs when the organization doesn’t produce broad and general data or inputs previously prejudiced data.

Question 15

Algothrim Bias

Answer 15

Occurs when algorithm design favors certain outcomes over others.

Question 16

Personalization

Answer 16

Uses: Tailoring products, services, and content to individual preferences and needs.
Key Principles: Consent, transparency, control, security, and fairness.

Question 17

Privacy and Data Protection Laws

Answer 17

GDPR (General Data Protection Regulation)-
Comprehensive data protection law since 2018.
Protects EU residents’ info from businesses.
Fines up to €20 million or 4% of business revenue.

CCPA (California Consumer Privacy Act): Regulates how businesses handle personal info of California residents.

Question 17

Principle of Least Privilege

Answer 17

Giving users and systems the minimal level of access necessary to perform their job functions.

Benefits: Reduces attack surface, maintains data confidentiality.

Question 18

Impacts of Poor Data Quality

Answer 18

-Biased outcomes.
-Ethical and social repercussions.
-Reputational damage.
-Decreased user adoption.
-Increased operational costs.
-Misguided business decisions.
-Compromised system performance.

Question 19

Benefits of Good Data Quality

Answer 19

Accurate targeting of customers.
Effective lead scoring and routing.
Better cross-sell and upsell opportunities.
Valuable insights on accounts.
Increased adoption and trust.

Question 20

Implementating Data Quality

Answer 20

Required Fields: Ensure critical information is captured.

Field Types: Use appropriate data types.

Validation Rules: Enforce data integrity.

Workflow Rules: Automate data processes.

Duplicate Management: Prevent and resolve duplicates.

Page Layouts: Optimize user interface.

Data Quality Dashboard: Monitor data quality metrics.

Data Enrichment Apps: Enhance data with additional information.

Question 20

Developing a Data Quality Management Plan

Answer 20

Naming Conventions: Rules for records.

Formatting: Standards for dates and currency.

Workflow: Stages of a record’s lifecycle (creation, update, review, archiving).

Quality Standards: Measure age, completeness, accuracy, duplication, usage.

Roles and Ownership: Assign responsibilities for record changes.

Security and Permissions: Determine levels of privacy and data access.

Monitoring Process: Ensure data quality.

Question 21

Salesforce AI Tools
Lightning Platform

Answer 21

Salesforce AI Tools
GPT
Vision
Prediction Builder
Next Best Action
Language
Recommendation Builder
Ecommerce
Product Recommendation
Commerce Insights

Question 22

Salesforce AI Tools
Einstein Sales

Answer 22

Lead Scoring
Opportunity Scoring
Forecasting
Account Insights
Activity Capture
Call Summary

Question 23

Salesforce AI Tools
Einstein Services

Answer 23

Bots
Case Classification
Case Routing
Article Recommendations
Case Recommendations

Question 24

Salesforce AI Tools
Einstein Marketing

Answer 24

Engagement Scoring
Engagement Frequency

Question 25

AI

Answer 25

AI is the branch of computer science focused on developing machines capable of performing tasks that typically require human intelligence.

Question 26

Stages of AI Evolution

Answer 26

Narrow (Weak) AI: Performs a single human cognitive task effectively (e.g., ChatGPT).

General (Strong) AI: Matches the cognitive capabilities of a human being (e.g., “I, Robot”).

Superintelligent AI: Surpasses human intelligence.

Question 27

Human Cognitive Abilities in AI

Answer 27

Learning
Perception
Reasoning
Language
Understanding

Question 29

Functional Components of AI Solutions
Computer Vision

Answer 29

Enables machines to interpret and process visual data from the world, automating tasks or enhancing decision-making through digital image analysis.

Question 30

Functional Components of AI Solutions

Natural Language Processing NLP

Answer 30

The intersection of linguistics and computer science, enabling computers to understand, interpret, and generate human language meaningfully.

Question 31

Functional Components of AI

Natural language Understanding (NLU)

Answer 31

Converts unstructured data (human language) into a structured format.
Techniques: Syntax analysis (parsing grammatical structure) and semantic analysis (interpreting meaning through context).

Question 32

Functional Components of AI

Natural language Generation (NLG)

Answer 32

Transforms structured information into human-like language.
Processes: Data structuring, lexicalization, and text realization.

Question 33

Modeling

Answer 33

Modeling: Algorithms create models to make predictions or decisions based on new data.

Question 34

Deep Learning

Answer 34

A subset of ML using multiple layers of neural networks to analyze various data input factors.

Question 34

Machine Learning (ML)

Answer 34

Involves creating algorithms that can modify themselves without human intervention by learning from data.

Question 35

Training:

Answer 35

Algorithms are trained using large datasets (training data) to learn data functions.
Example: Email filtering.

Question 35

Neural Network

Answer 35

Algorithms that model and operate like the human brain, interpreting sensory data.

Question 36

Robotics

Answer 36

The design, operation, and construction of robots to assist or replace human efforts.

Core Components: Sensors, actuators, and control systems.

Example: Automated vacuum cleaner.

Question 37

Generative AI

Answer 37

Generates new content (text, images, videos, music) by learning from large datasets using models like Generative Adversarial Networks (GANs). It innovates by mimicking existing styles but is resource-intensive and complex.

Question 38

Predictive AI

Answer 38

Uses statistical algorithms to analyze historical data to make future predictions, identifying patterns and trends. It requires quality historical data and is less demanding compared to generative AI.