How to Pass Salesforce Certified AI Associate Exam

The Salesforce Certified AI Associate should be able to provide informed strategies and guide stakeholder decisions based on Salesforce’s Trusted AI Principles. Candidates should be familiar with data management, security considerations, common business and productivity tools, and Salesforce Customer 360.

1. About the Salesforce Certified AI Associate Exam

	Content	40 multiple-choice
	Time allotted	70 minutes
	Passing score	65% (26 out of 40 questions)
	Exam Fee	USD 75 plus applicable taxes
	Retake Fee	Free
	Prerequisite	None

For up to date information about this certification please refer to the official exam guide here!

2. Salesforce Salesforce Certified AI Associate Exam Outline

2.1 AI Fundamentals: 17%

• Explain the basic principles and applications of AI within Salesforce.
• Differentiate between the types of AI and their capabilities.

2.2 AI Capabilities in CRM: 8%

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

2.3 Ethical Considerations of AI: 39%

• Describe the ethical challenges of AI (e.g., human bias in machine learning, lack of transparency, etc.).
• Apply Salesforce’s Trusted AI Principles to given scenarios.

2.4 Data for AI: 36%

• Describe the importance of data quality.
• Describe the elements/components of data quality.

3. Salesforce Salesforce Certified AI Associate Exam Study Course

• Trailmix: Prepare for Your Salesforce AI Associate Credential
• Cert Prep: Salesforce AI Associate
• Free Practice Questions

4. Important Topics for the Salesforce Certified AI Associate Exam

2.1 AI Fundamentals: 17% ( 7 Questions)

• Main Types of AI Capabilities
  • Numeric Predictions
  • Classifications
  • Robotic Navigation
  • Language Processing
• Neural networks are tools for training AI models.
• Training AI by adding extra layers to find hidden meaning in data is called deep learning.
• Most important components of AI:
  • Natural language understanding (NLU) refers to systems that handle communication between people and machines.
  • Natural language processing (NLP) is distinct from NLU and describes a machine’s ability to understand what humans mean when they speak as they naturally would to another human.
  • Named entity recognition (NER) labels sequences of words and picks out the important things like names, dates, and times. NER involves breaking apart a sentence into segments that a computer can understand and respond to quickly.
  • Deep learning refers to artificial neural networks being developed between data points in large databases.
• Generative AI is technology that takes a set of data and uses it to create something new – like poetry, a physics explainer, an email to a client, an image, or new music – when prompted by a human. 
• Generative AI uses two types of deep learning models:
  • GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator.
  • Transformer models, like ChatGPT, (which stands for Chat Generative Pretrained Transformer), create outputs based on sequential data (like sentences or paragraphs) rather than individual data points. This approach helps the model efficiently process context and is why it’s used to generate or translate text.
• Common Concerns About Generative AI:
  • Hallucinations: Predictions from generative AI that diverge from an expected response, grounded in facts, are known as hallucinations. They happen for a few reasons, like if the training data was incomplete or biased, or if the model was not designed well.
  • Data security: Businesses can share proprietary data in the generative AI lifecycle. Companies that offer AI services must demonstrate that trust is paramount and that data will always be protected.
  • Plagiarism: LLMs and AI models for image generation are typically trained on publicly available data. There’s the possibility that the model will learn a style and replicate that style.
  • User spoofing: Fake users generated with AI can interact with real users in a very realistic way. That makes it hard for businesses to identify bot networks that promote their own bot content.
  • Sustainability: The computing power required to train AI models is immense, and the processors doing the math require a lot of actual power to run. As models get bigger, so do their carbon footprints.
• Generative AI Glossary
• Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions.

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Read More: Free Salesforce Certified AI Associate Practice Questions

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Generative AI	Predictive AI
Generative AI combines algorithms and deep learning neural network techniques to generate content that is based on the patterns it observes in other content.	Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions.
Used for producing fresh content such as Text, Images, Product Design, Personalization	Used for financial forecasting, fraud detection, healthcare and marketing.

• Natural language processing (NLP), is a field of artificial intelligence (AI) that combines computer science and linguistics to give computers the ability to understand, interpret, and generate human language in a way that’s meaningful and useful to humans.
• Generative AI Terms

Generative AI Terms	What it means for customers	What it means for teams
Artificial intelligence (AI)	AI can help your customers by predicting what they’re likely to want next, based on what they’ve done in the past.	AI helps your teams work smarter and faster by automating routine tasks.
Artificial neural network (ANN)	Customers benefit in all sorts of ways when ANNs are solving problems and making accurate predictions.	Teams can forecast customer churn, which prompts proactive ways to improve customer retention.
Augmented intelligence	Augmented intelligence lets a computer crunch the numbers, but then humans can decide what actions to take based on that information.	Augmented intelligence can help you make better and more strategic decisions.
CRM with generative AI	A CRM gives customers a consistent experience across all channels of engagement, from marketing to sales to customer service and more.	A CRM helps companies stay connected to customers, streamline processes, and improve profitability.
Deep learning	Deep learning-powered CRMs create opportunities for proactive engagement. They can enhance security, make customer service more efficient, and personalize experiences.	In a CRM system, deep learning can be used to predict customer behavior, understand customer feedback, and personalize product recommendations.
Discriminator (in a GAN)	Discriminators in GANs are an important part of fraud detection, so their use leads to a more secure customer experience.	Discriminators in GANs help your team evaluate the quality of synthetic data or content. They aid in fraud detection and personalized marketing.
Ethical AI maturity model	Helps build trust and assures your customers that you are using their data in responsible ways.	Regularly evaluating your AI practices and staying transparent about how you use AI can help you stay aligned to your company’s ethical considerations and societal values.
Explainable AI (XAI)	If an AI system can explain its decisions in a way that customers understand, it increases reliability and credibility.	XAI can help employees understand why a model made a certain prediction. Not only does this increase their trust in the system, it also supports better decision-making and can help refine the system.
Generative AI	Better and more targeted marketing content, which helps them get exactly the information they need and no more.	Faster builds for marketing campaigns and sales motions, plus the ability to test out multiple strategies across synthetic data sets and optimize them before anything goes live.
Generative adversarial network (GAN)	They allow for highly customized marketing that uses personalized images or text.	They can help your development team generate synthetic data when there is a lack of customer data.
Generative pre-trained transformer (GPT)	Customers have more personalized interactions with your company that focus on their specific needs.	GPT could be used to automate the creation of customer-facing content, or to analyze customer feedback and extract insights.
Generator	Using generators, it’s possible to train AI chatbots that learn from real customer interactions, and continuously create better and more helpful content.	Generators can be used to create realistic datasets for testing or training purposes. This can help your team find any bugs in a system before it goes live, and let new hires get up to speed in your system without impacting real data.
Hallucination	When companies monitor for and address this issue in their software, the customer experience is better and more reliable.	Quality assurance will still be an important part of an AI team. Monitoring for and addressing hallucinations helps ensure the accuracy and reliability of AI systems.
Large language model (LLM)	Personalized chatbots that offer human-sounding interactions, allowing customers quick and easy solutions to common problems in ways that still feel authentic.	Teams can automate the creation of customer-facing content, analyze customer feedback, and answer customer inquiries.
Machine learning	When a company better understands what customers value and want, it leads to enhancements in current products or services, or even the development of new ones that better meet customer needs.	Machine learning can be used to predict customer behavior, personalize marketing content, or automate routine tasks.
Machine learning bias	Working with companies that actively engage in overcoming bias leads to more equitable experiences, and builds trust.	It’s important to check for and address bias to ensure that all customers are treated fairly and accurately. Understanding machine learning bias and knowing your organization’s controls for it helps your team have confidence in your processes.
Model	The model can help customers get much more accurate product recommendations.	This can help teams to predict customer behavior, and segment customers into groups.
Natural language processing (NLP)	NLP allows customers to interact with systems using normal human language rather than complex commands.	NLP can be used to analyze customer feedback, power chatbots, or automate the creation of customer-facing content.
Prompt engineering	When your generative AI tool gets a strong prompt, it’s able to deliver a strong output. The stronger, more relevant the prompt, the better the end user experience.	Can be used to ask a large language model to generate a personalized email to a customer, or to analyze customer feedback and extract key insights.
Sentiment analysis	Customers can offer feedback through new channels, leading to more informed decisions from the companies they interact with.	Sentiment analysis can be used to understand how customers feel about a product or brand, based on their feedback or social media posts, which can inform many aspects of brand or product reputation and management.
Supervised learning	Increased efficiency and systems that learn to understand their needs via past interactions.	Can be used to predict customer behavior or segment customers into groups, based on past data.
Transformer	Businesses can enhance the customer service experience with personalized AI chatbots.	Transformers help your team generate customer-facing content, and power chatbots that can handle basic customer interactions.
Unsupervised learning	When we uncover hidden patterns or segments in customer data, it enables us to deliver completely personalized experiences.	Teams get valuable insights and a new understanding of complex data. It enables teams to discover new patterns, trends, or anomalies that may have been overlooked, leading to better decision-making and strategic planning.
Validation	Better-trained models create more usable programs, improving the overall user experience.	Can be used to ensure that a model predicting customer behavior or segmenting customers will work as intended.
Zone of proximal development (ZPD)	When your generative AI is trained properly, it’s more likely to produce accurate results.	Can be applied to employee training so an employee could learn to perform more complex tasks or make better use of the CRM’s features.