The Salesforce AI Associate exam preparation guide is designed to provide candidates with necessary information about the AI Associate exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Salesforce Certified AI Associate exam.
It is recommended for all the candidates to refer the AI Associate objectives and sample questions provided in this preparation guide. The Salesforce AI Associate certification is mainly targeted to the candidates who want to build their career in Salesforce Associate domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Salesforce AI Associate exam.
Salesforce AI Associate Exam Summary:
|
Exam Name
|
Salesforce AI Associate |
| Exam Code | AI Associate |
| Exam Price |
Registration fee: USD 50 Retake fee: Free |
| Duration | 70 minutes |
| Number of Questions | 40 |
| Passing Score | 65% |
| Recommended Training / Books |
Prepare for Your Salesforce AI Associate Credential Salesforce AI Associate Certification Prep |
| Schedule Exam | Kryterion Webassessor |
| Sample Questions | Salesforce AI Associate Sample Questions |
| Recommended Practice | Salesforce Certified AI Associate Practice Test |
Salesforce AI Associate Syllabus:
| Section | Objectives | Weight |
|---|---|---|
| AI Fundamentals |
- Explain the basic principles and applications of AI within Salesforce.
- Differentiate between the types of AI and their capabilities. |
17% |
| AI Capabilities in CRM |
- Identify CRM AI capabilities.
- Describe the benefits of AI as they apply to CRM. |
8% |
| Ethical Considerations of AI |
- Describe the ethical challenges of AI (for example, human bias in machine learning, lack of transparency, etc.).
- Apply Salesforce's Trusted AI Principles to given scenarios. |
39% |
| Data for AI |
- Describe the importance of data quality.
- Describe the elements/components of data quality. |
36% |