Salesforce AI Associate

Certification Guide

The AI Associate Certification focuses on the basic principles and functionalities of AI and data within the CRM context. It is designed for individuals with varying backgrounds, including business leaders and professionals familiar with AI, whether at an introductory or more advanced level. This certification aims to confirm essential skills in the responsible use of AI and data, creating a pathway for pursuing advanced technical certifications in the same field.

Key Facts

The exam is made up of 40 multiple choice questions

70 minutes to complete

The passing score is 65%

There are no prerequisites

Cost is USD $75 and the retake is free if you are unsuccessful

This information will assist you if you’re interested in becoming AI Associate certified and includes an overview of the core topics in the exam.

There are 4 areas of knowledge that are covered by the Salesforce AI Associate certification.



AI Fundamentals


AI Capabilities in CRM


Ethical Considerations of AI


Data for AI


AI Associate Topic Weighting Chart

AI Associate
Certification Contents

The following are the core topic areas of the  AI Associate certification and what you’re expected to know:

AI Fundamentals

This topic includes the following objectives:

  • Explain the basic principles and applications of AI within Salesforce.

         Artificial Intelligence (AI) involves replicating intellectual prowess within machines, empowering them to execute tasks that traditionally demand human cognitive abilities, including learning, problem-solving, and decision-making. The suite of AI tools known as Einstein within the Salesforce ecosystem stands at the forefront of technological innovation, redefining how organizations harness the power of artificial intelligence. At its core, Einstein is not merely a feature but a comprehensive suite of capabilities designed to intelligently process, analyze, and leverage data within the Salesforce platform.

  • Differentiate between the types of AI and their capabilities.

       Generative AI and predictive AI are distinct forms of artificial intelligence with unique capabilities. Generative AI utilizes advanced modeling to generate images, text, video, and software code, and its applications can be found in creative fields such as literature, art, or music. On the other hand, predictive AI leverages large data repositories to recognize patterns over time, and suggest future outcomes that can be applied in finance, healthcare, and marketing. Both generative and predictive AI rely on machine learning but cater to different domains and tasks.

AI Capabilities in CRM

This topic includes the following objectives:

  • Identify CRM AI capabilities.

          In today's business landscape, AI in CRM is crucial for driving customer engagement, sales, marketing, and service excellence. This section explores AI's transformative impact on CRM, covering enhanced sales, automated marketing, customer service, and insights. Introduction to AI basics, personalized interactions, and Salesforce's AI tools, including Einstein Prediction Builder and Einstein Bots, will be covered. By the end, the audience will understand CRM's AI capabilities comprehensively and how to leverage them for improved customer relationships and organizational success.

  • Describe the benefits of AI as they apply to CRM.

         The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) offers unparalleled advantages. Businesses gain a competitive edge through AI-powered CRM, ensuring superior customer experiences, data-driven decision-making, and adaptability to market trends. This section highlights the benefits of AI in CRM, which include gaining a strategic competitive advantage, enhancing operational efficiency, making data-driven decisions, fostering improved customer engagement, optimizing sales and marketing processes, and delivering enhanced customer service. These advantages collectively empower organizations to thrive in the dynamic landscape of modern business.

Ethical Considerations of AI

This topic includes the following objectives:

  • Describe the ethical challenges of AI (e.g., human bias in machine learning, lack of transparency, etc.).

            Ethical considerations in AI refer to the systematic evaluation and incorporation of moral principles and values into developing, deploying, and using artificial intelligence systems. This entails a conscious effort to assess the potential impact of AI technologies on individuals, communities, and society. Ethical considerations aim to ensure that AI applications align with fundamental principles such as fairness, transparency, accountability, and respect for privacy, thereby promoting responsible and conscientious practices throughout the entire lifecycle of AI systems.

  • Apply Salesforce's Trusted AI Principles to given scenarios.

     In the dynamic landscape of artificial intelligence, ethical considerations play a pivotal role in shaping responsible and trustworthy AI systems. Salesforce places a paramount emphasis on ethics in AI development. The commitment to ethical AI is encapsulated in its Trusted AI Principles. These principles serve as a guiding framework, emphasizing the company's dedication to developing AI that is not only innovative but also aligned with ethical standards. The Trusted AI Principles at Salesforce encompass five key pillars – Responsible, Accountable, Transparent, Empowering, and Inclusive. Each principle is designed to address specific aspects of ethical AI development, fostering a culture of trust and responsibility in the deployment of AI technologies.

Data for AI

This topic includes the following objectives:

  • Describe the importance of data quality.

       Data quality is paramount for businesses as it directly influences the accuracy and reliability of insights derived from data. Implementing effective data management tools is key to maintaining high-quality data, involving practices such as setting up validation rules, employing data enrichment tools, and managing duplicates.
In the realm of AI analysis and applications, the impact of data quality is profound as accurate and reliable data ensures that AI models are trained effectively. High-quality data is the foundation for unlocking the full potential of AI, making it essential for businesses to prioritize robust data management practices.

  • Describe the elements/components of data quality.

      Understanding the elements and components of data quality is essential as it enables businesses to assess, improve, and maintain the reliability and accuracy of their data. High-quality data forms a robust foundation for accurate AI model training.
This section discusses the fundamentals of data quality and the importance of having a comprehensive framework for evaluating and ensuring data quality. Useful data traits contribute to accurate analysis and the generation of meaningful insights.

To prepare successfully for the certification exam, we recommend to work through our

AI Associate Study Guide and AI Associate Practice Exams

AI Associate
Study Guide

Every topic objective explained thoroughly.
The most efficient way to study the key concepts in the exam.

AI Associate

Practice Exams

Test yourself with complete practice exams or focus on a particular topic with the topic exams. Find out if you are ready for the exam.

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Copyright 2024 -


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