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Exam Information
Official specifications published by Salesforce
Exam Format
Registration
Skills this practice test covers
- Tell predictive analytics, machine learning, natural language processing and computer vision apart.
- Match Einstein features in Sales, Service and Marketing Cloud to the CRM task each one helps with.
- Spot human bias, missing transparency and privacy risks in an AI use case.
- Apply Salesforce's Trusted AI Principles to a business scenario.
- Judge whether CRM data is accurate, complete and consistent enough for AI.
Explore related topics
AI-Associate exam content
4 domains · 9 topics · 45 of 113 questions sorted by domain
AI Fundamentals7 questions
This domain sets out the basic ideas. Candidates explain how AI is used within Salesforce and compare kinds of AI such as predictive analytics, machine learning, natural language processing and computer vision.
Skills tested
- Explain the basic principles and applications of AI within Salesforce.
- Compare predictive analytics, machine learning, NLP and computer vision by what each one can do.
- Tell classification from regression in a simple prediction task.
Questions by topic
- Types of AI and Their Capabilities7
AI Capabilities in CRM3 questions
This smaller domain links AI to daily CRM work. Candidates identify Einstein capabilities across Salesforce products and describe the benefits, such as faster responses and better decisions.
Skills tested
- Identify Einstein features such as Einstein Lead Scoring, Einstein Bots and Einstein Case Classification.
- Describe the business benefits of AI in CRM, such as higher productivity and personalized customer experiences.
Questions by topic
- CRM AI Capabilities2
- Describe the benefits of AI as they apply to CRM.1
Ethical Considerations of AI19 questions
The largest domain asks how AI can cause harm and how Salesforce expects teams to prevent it. Candidates describe challenges such as human bias and lack of transparency, and apply the Trusted AI Principles to scenarios.
Skills tested
- Explain how human bias enters training data, including selection bias and confirmation bias.
- Describe the black box problem and why explainability matters.
- Recognize privacy and security risks, such as data poisoning or missing consent.
- Apply Salesforce's Trusted AI Principles to a given scenario.
- Explain why a human in the loop keeps people accountable for AI decisions.
Questions by topic
- Human Bias in Machine Learning4
- Applying Salesforce's Trusted AI Principles12
- Ethical Challenges of AI3
Data for AI16 questions
AI is only as good as its data, and this domain tests that idea. Candidates describe why data quality matters and name its elements, such as accuracy, completeness and consistency.
Skills tested
- Describe how poor data quality lowers the accuracy of AI predictions.
- Check records for accuracy, completeness, consistency and timeliness.
- Find duplicate records and missing values that would mislead a model.
Questions by topic
- Importance of Data Quality5
- Elements and Components of Data Quality8
- Data Preparation and Governance3
Requirements
- No formal prerequisites.
- Recommended, not required: Salesforce Certified Platform Foundations, for a base in the Salesforce Customer 360 platform.
- Recommended: basic knowledge of AI and of Salesforce, at the level of a newcomer with up to six months of experience.
About the AI-Associate exam
AI-Associate is Salesforce's foundational exam on artificial intelligence in customer relationship management (CRM) software. It suits people with up to six months of AI and Salesforce experience. Candidates explain AI types and the AI features of Salesforce Einstein, describe ethical risks, apply Salesforce's Trusted AI Principles and judge data quality. Ethics and data quality make up most of the exam, and passing earns the Salesforce Certified AI Associate credential.
Many questions in the PlanetCert practice test give a short business scenario and ask which Trusted AI Principle applies. The explanations show why one principle fits the case better than another. Flashcard mode suits the data quality terms and the Einstein feature names. The practice test also includes questions on Agentforce agents and prompt templates, which go beyond the four exam domains and follow the Agentforce Specialist outline. Brainy AI Assistant can explain how biased training data leads to an unfair prediction.
How to earn the certification
- Earns
- Salesforce Certified AI AssociateAssociate
- Before
- PLAT-101 Salesforce Certified Platform Foundations
Recommended by Salesforce, not required, for a base in the Customer 360 platform.
- PLAT-101 Salesforce Certified Platform Foundations
- After
- AI-201-2 Salesforce Certified Agentforce Specialist
Salesforce's specialist exam for building, testing and deploying Agentforce agents, a next step for Salesforce AI skills.
- AI-201-2 Salesforce Certified Agentforce Specialist
How to prepare for AI-Associate
- 1
Work through the Trailhead prep
Salesforce's own preparation is its AI Associate trailmix and the Salesforce AI Associate Certification Prep module on Trailhead. Finish both before drilling questions.
- 2
Sort the AI types by example
Write one Salesforce example for predictive analytics, machine learning, natural language processing and computer vision, such as lead scoring or sentiment analysis. Many AI Fundamentals questions turn on telling these apart.
- 3
Argue each principle with a case
Ethical Considerations of AI is the largest domain. For each Trusted AI Principle, write a short case in which a team breaks it, and the fix that would follow it.
- 4
Audit a small customer data set
Take a short list of customer records and mark every missing field, duplicate and out-of-date value. This is the kind of judgment that Data for AI questions ask for.
- 5
Review misses with Brainy AI Assistant
Take the AI-Associate practice test in Study Mode, then once in Timed Mode. For each missed ethics question, ask Brainy AI Assistant why the chosen principle did not fit the case.
Start a timed AI-Associaterun
Salesforce recommends its AI Associate preparation trailmix and the Salesforce AI Associate Certification Prep module on Trailhead.
Who AI-Associate is for
- Salesforce admins, consultants and business users who are new to AI.
- Newcomers to Salesforce who want a plain base in AI, ethics and data quality.
- Team leads who judge AI use cases in their CRM for risk and data readiness.
Typical job titles: Salesforce administrator · Salesforce consultant · Business analyst · CRM specialist
AI-Associate glossary
19 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- Black Box Problem
- The difficulty of seeing how an AI model reached its result, which makes its decisions hard to explain.
- Completeness
- A data quality element: how many of the required fields and records are actually filled in.
- Computer Vision
- AI that interprets images and video, for example to recognize objects.
- Confirmation bias
- The habit of favoring information that supports an existing belief, which can shape how data is chosen.
- Data poisoning
- An attack that slips false records into training data so that the model learns the wrong patterns.
- Data stewardship
- The ongoing care of a data set's quality and use, with a named owner who keeps it accurate.
- Disparate impact
- An unfair outcome in which a model's decisions harm one group more than others, even without intent.
- Duplicate records
- Two or more records for the same customer or item, which confuse reports and AI models.
- Einstein Bots
- Salesforce Einstein chatbots that answer routine customer questions and pass harder cases to people.
- Einstein Lead Scoring
- A Salesforce Einstein feature that scores leads by how likely they are to convert.
- Einstein Prediction Builder
- A Salesforce tool for building custom AI predictions on Salesforce data without writing code.
- Garbage in, garbage out
- The idea that a model trained on poor data will give poor results.
- Human in the loop
- A design in which a person reviews or approves AI output before it takes effect.
- Machine Learning
- A branch of AI in which a system learns patterns from data instead of following fixed rules.
- Natural Language Processing (NLP)
- AI that understands and produces human language, as chatbots and sentiment analysis do.
- Predictive Analytics
- Using historical data to forecast likely outcomes, such as which leads will convert.
- Selection bias
- Bias that appears when training data does not represent the people or cases the model will judge.
- Sentiment analysis
- Using NLP to judge whether a piece of text sounds positive, negative or neutral.
- Trusted AI Principles
- Salesforce's principles for building and using AI responsibly, which candidates apply to scenarios on the exam.
Related practice tests
- ADM-201Salesforce Certified Platform Administrator
- PLAT-101Salesforce Certified Platform Foundations
- ADM-211Administration Essentials for Experienced Admin
- AP-202B2B Commerce for Developers Accredited Professional
- Comm-Dev-101B2C Commerce Cloud Developer