Salesforce Certified AI Associate

Explain the key concepts of artificial intelligence and machine learning (supervised, unsupervised, reinforcement).

Describe the difference between AI, ML, deep learning, and generative AI.

Explain common AI use cases in business (natural language processing, computer vision, recommendation engines).

Identify the potential risks and limitations of AI including bias, hallucination, and data quality issues.

Identify the AI-powered features available in Sales Cloud (Einstein Lead Scoring, Opportunity Scoring).

Identify the AI-powered features available in Service Cloud (Einstein Case Classification, Article Recommendations).

Describe the capabilities of Einstein for Marketing Cloud (Send-Time Optimization, Einstein Engagement Scoring).

Identify the capabilities of Einstein for Flow, Einstein Bots, and Agentforce agents.

Describe the capabilities of Einstein GPT / Einstein Copilot for generating content and drafting responses.

Explain the ethical principles guiding the responsible use of AI (transparency, accountability, fairness).

Identify potential biases in AI systems and strategies to mitigate them.

Describe data privacy considerations when using AI (data governance, consent, anonymization).

Explain Salesforce's Trusted AI Principles (responsible, accountable, transparent, empowering, inclusive).

Describe the importance of data quality for AI outcomes (accuracy, completeness, consistency, timeliness).

Identify how Data Cloud enables AI with harmonized, real-time customer data.

Explain the role of feature engineering and data labeling in training effective ML models.

Describe the concept of grounding AI with structured and unstructured enterprise data for RAG scenarios.