Salesforce Certified AI Associate Exam _2

AI technique using interconnected nodes to recognize patterns and predict outcomes. Think of a web of connections, each node is affected by others' 'weights' and 'biases'.

AI application that uses historical data to predict future outcomes, like sales trends or customer churn. Think of forecasting the future based on past patterns.

A system that uses predefined rules and logic to make decisions. Less adaptable than AI, like a set of instructions for a specific task.

A principle that restricts access to sensitive data to only those who need it, reducing risk of unauthorized access and misuse. Like keeping a vault secure.

Poor data quality can cause AI systems to learn biases, leading to inaccurate predictions and unfair outcomes. Like building a house on a shaky foundation.

Personalizing customer experiences based on their preferences can improve satisfaction, loyalty, and overall engagement. Think of tailored recommendations for a better experience.

A data quality assessment helps companies understand the accuracy and reliability of their data, improving AI model performance and business outcomes. Like a health check for your data.

Poor data quality introduces inaccuracies and inconsistencies, leading to unreliable AI predictions and potentially biased results. Like using inaccurate ingredients in a recipe.

Data quality impacts the trust and reliability of AI-driven decisions. High-quality data leads to more credible and accurate outcomes. Think of building trust based on solid evidence.

Predictive AI uses data to predict outcomes, like classifying customers or forecasting sales, while generative AI creates new content, like generating product descriptions or composing music.

User consent is crucial for ethical AI data usage. Users should be informed and agree to how their data is collected and used, protecting privacy. Think of informed permission for data sharing.

To maintain data consistency, use predefined values or naming conventions to ensure all records are entered and used consistently across different accounts. Like using a standardized template for consistent results.

Duplicate records can negatively impact data quality, creating confusion and inaccuracies in AI analysis. Think of having multiple copies of the same information.

Introducing bias in training data can create inaccurate AI models that reflect prejudices from the data. Like learning from a biased teacher.

To prevent bias, avoid using demographic data that can lead to unfair or discriminatory outcomes. Think of focusing on relevant data, not personal traits.

Collaboration between humans and AI systems leads to better decision-making by combining human expertise with AI insights, creating a more balanced approach. Think of a team of experts with different skills.

Inconsistencies in data entry practices can lead to inaccuracies and make it difficult for AI models to interpret data correctly. Think of different recipes leading to different outcomes.

Data quality is crucial for training accurate and reliable AI models, ensuring optimal performance and reliable predictions. Think of a strong foundation for a sturdy building.

For AI models with large datasets, prioritize volume to ensure sufficient training data. Think of having a lot of ingredients for a good recipe.

Einstein Bots use AI to create interactive chat experiences, helping customers find answers quickly and efficiently. Think of a virtual assistant helping customers find answers.

Use the Text field type in Salesforce to capture customer preferences, like preferred names, providing flexibility and free-form input. Think of a space for open-ended responses.

Testing AI models with diverse datasets ensures inclusivity and avoids bias, protecting the interests of all potential users. Think of testing a product with a variety of users.

Leveraging AI capabilities like Einstein Next Best Action and Case Auto Response Rules can enhance sales and customer support processes. Think of AI tools that automate tasks and improve outcomes.

AI assists in lead qualification by using customer data to score leads based on their likelihood of converting. Think of ranking potential customers based on their interest.

Incomplete data can lead to inaccurate predictions and recommendations, impacting business decisions and customer experiences. Think of missing ingredients in a recipe.

Duplicate management tools help identify and merge duplicate records, improving data quality and ensuring accuracy in AI analysis. Think of cleaning up duplicate files to avoid confusion.

Data inconsistencies should be addressed promptly. Investigate and correct errors, ensuring the data is accurate and reliable for AI training. Think of correcting mistakes to avoid errors.

Call Summaries use AI to automatically generate summaries of past customer calls, saving time and improving sales representative efficiency. Think of automatic notes from conversations.

Leveraging data quality apps on AppExchange provides a quick and easy way to assess data quality, identifying inconsistencies and potential issues. Think of a tool for quickly evaluating data health.

Salesforce automatically excludes sensitive demographic data from training models to prevent discrimination and ensure fair treatment of all users. Think of protecting user privacy and equity.

Salesforce's Trusted AI Principle of Transparency requires providing clear explanations for AI predictions and model behaviors, increasing user trust and understanding. Think of transparently explaining how decisions are made.

To prevent bias, import diverse and representative training data that reflects the real-world population, ensuring fairness and accuracy in AI models.

Ignoring ethics in AI development can lead to harmful consequences for individuals and society, like perpetuating biases or infringing on privacy.

Ethical debt occurs when an organization chooses to launch an AI product that contains known biases or flaws, delaying a necessary fix.

Consequence Scanning Workshops help test AI models with different datasets to identify potential biases and ensure inclusivity. Think of preemptively finding potential issues.

The Trusted AI Principle of Empowerment aims to democratize AI, enabling users of all levels to build and use AI applications, regardless of technical expertise. Think of accessibility for everyone.

To ensure safe and reliable AI applications, create guardrails to prevent harm to users and sensitive data, while promoting positive outcomes. Think of safety checks and boundaries.

AI applications can streamline sales, enhance customer service, and provide personalized experiences, leading to improved business outcomes. Think of AI tools that optimize business processes.

Call summaries, generated by AI, provide insights into past customer interactions, helping sales representatives understand context and personalize their approach. Think of a recap of past conversations.

A data quality assessment starts with identifying business objectives to ensure that the data analysis aligns with company goals. Think of understanding the purpose of the data analysis.

Immutable traits, such as financial status, cannot be changed easily, and can be used to unfairly categorize individuals. Think of characteristics that are difficult to modify.