How to Pass Salesforce Certified AI Associate Exam

2.2 AI Capabilities in CRM: 8% ( 3 Questions)

• Einstein allows all Salesforce users to:
  • Discover insights that bring new clarity about your company’s customers.
  • Predict outcomes so your users can make decisions with confidence.
  • Recommend the best actions to make the most out of every engagement.
  • Automate routine tasks so your users can focus on customer success.
  • Generate tailored content – from emails, to knowledge articles, to code.

• Einstein Discovery enables you to: 
  • Identify, surface, and visualize insights into your business data.
  • Predict future outcomes and suggest ways to improve predicted outcomes in your workflows.
• Einstein Discovery-powered solutions address these use cases:
  • Regressions for numeric outcomes represented as quantitative data (measures), such as currency, counts, or any other quantity.
  • Binary classification for text outcomes with only two possible results. These are typically yes or no questions that are expressed in business terms, such as churned or not churned, opportunity won or lost, employee retained or not retained, and so on.
  • Multiclass classification for text outcomes with 3 to 10 possible results. For example, a manufacturer can predict, based on customer attributes, which of five service contracts a customer is most likely to choose.
• You can use Einstein Prediction Service to:
  • Get predictions on your data.
  • Get suggested actions to take to improve predicted outcomes.
  • Manage prediction definitions and models that are deployed in Salesforce.
  • Manage bulk scoring jobs.
  • Manage model refresh jobs.
• A prediction is a derived value, produced by a model, that represents a possible future outcome based on a statistical understanding of past outcomes plus provided input values (predictors). 
• An outcome is the business result you are trying to understand and improve. An outcome is typically a key performance indicator (KPI), such as sales margin or opportunity wins.
• A prediction represents an output value that the model generates based on the provided input values (predictors). The model’s equation is the result of a thorough statistical analysis of past data with known outcomes, powered by machine learning and AI.
• A model is the sophisticated, custom mathematical construct that Einstein Discovery generates. Einstein Discovery uses a model to predict an outcome. 
• A model organizes data by variables. A variable is a category of data. It’s analogous to a column in a CRM Analytics dataset or a field in a Salesforce object. A model has inputs (predictor variables) and outputs predictions for the outcome variable, along with additional information if requested. 
• Predictions occur at the observation level. An observation is a structured set of data. It’s analogous to a populated row in a CRM Analytics dataset or a record in a Salesforce object. 

2.3 Ethical Considerations of AI: 39% ( 16 Questions)

• Salesforce’s Trusted AI Principles
  • Responsible – Safeguard human rights and protect the data we are entrusted with.
  • Accountable – Seek and leverage feedback for continuous improvement.
  • Transparent – Develop a transparent user experience to guide users through machine driven recommendations. Be transparent about how AI is built.
  • Empowering – Promote economic growth and employment for our customers, their employees and society as a whole. Empower customers to use AI responsibly.
  • Inclusive – Respect the societal values of all those impacted, not just those of the creators.
• Five guidelines Salesforce is using to guide the development of trusted generative AI
  • Accuracy – Deliver verifiable results that balance accuracy, precision, and recall in the models by enabling customers to train models on their own data. Communicate when there is uncertainty about the veracity of the AI’s response and enable users to validate these responses.
  • Safety – Make every effort to mitigate bias, toxicity, and harmful output by conducting bias, explainability, and robustness assessments, and red teaming. 
  • Honesty – When collecting data to train and evaluate our models, we need to respect data provenance and ensure that we have consent to use data. Be transparent that an AI has created content when it is autonomously delivered.  
  • Empowerment – Identify the appropriate balance to “supercharge” human capabilities and make these solutions accessible to all. 
  • Sustainability – Develop right-sized models where possible to reduce our carbon footprint. 
• Ethical AI Practice Maturity Model
  • Ad Hoc
  • Organized & Repeatable
  • Managed & Sustainable
  • Optimized & Innovative
• Biggest perceived risks of real-time personalization in marketing:
  • Security events, like data breaches
  • Data being collected, shared, or used in unanticipated ways
  • Personalizing interactions that feel invasive or unwanted to consumers
  • Inadvertent bias introduced by relying on demographic attributes for interactions instead of behavioral and engagement data
• Responsible Marketing Principles when implementing personalization
  • Be Transparent About Security – Being transparent about partnership with your customers can help you cement their trust in–and loyalty to—your brand. 
  • Develop Trust Through Consent and Transparency – When building personalized experiences, make sure that you check and obtain consent before collecting data.
  • Use Data to Personalize the Experience – Providing better, more relevant experiences and, most importantly, ones that are driven by your customers’ intent. Within a curated experience, customers are more likely to share personal and sometimes sensitive information when the benefit to them is clear and understood. 
  • Prioritize Behavior-Based Intent Over Demographic Attributes – demographic targeting causes bias and fails to deliver the right messaging to the right people.

2.4 Data for AI: 36% ( 14 Questions)

• Factors that determine data quality
  • Missing Records 
  • Duplicate Records
  • No Data Standards
  • Incomplete Records
  • Stale Data 
• Bad data is consistently linked with:
  • Lost revenue
  • Missing or inaccurate insights
  • Wasted time and resources
  • Inefficiency
  • Slow info retrieval
  • Poor customer service
  • Reputational damage
  • Decreased adoption by reps
• Good data lets your company:
  • Prospect and target new customers
  • Identify cross-sell and upsell opportunities
  • Gain account insights
  • Increase efficiency
  • Retrieve the right info fast
  • Build trust with customers
  • Increase adoption by reps
  • Plan and align territories better
  • Score and route leads faster
• Data Quality Dimensions
  • Age – What was the last time each record was updated?
  • Completeness – Are all key business fields on records filled in?
  • Accuracy – Has it been matched against a trusted source?
  • Consistency – Is the same formatting, spelling, and language used across records?
  • Duplication – Are records and data duplicated in your org?
  • Usage – Is your data being harnessed in reports, dashboards, and apps?
• Standards for creating, processing, and maintaining data
  • Naming – Set naming conventions for records.
  • Formatting – Figure out how dates and money are represented.
  • Workflow – Determine processes for record creation, reviewing, updating, and archiving. Determine all the stages a record goes through during its life cycle.
  • Quality – Set appropriate standards for data quality, including the ability to measure or score records. 
  • Roles and Ownership – Determine the appropriate levels of privacy for data. Make sure to comply with regulatory, legal, and contractual obligations.
  • Monitoring – Outline a process for ensuring quality control of data. 
• Steps to clean your data to make it AI ready:
  1. Remove duplicate or irrelevant observations – Duplication happens when you combine data sets from multiple places, and duplicate entries are created.
  2. Fix structural errors – This happens when data includes typos, incorrect capitalization, or mislabelings.
  3. Filter unwanted outliers – There are often one-off observations that don’t appear to align with the data you’re analyzing. That might be the result of incorrect data entry.
  4. Handle missing data – Eliminate observations that include missing values, input missing values based on other observations or consider altering the way the data is used to effectively navigate the missing values.
  5. Validate – Does the data make sense, does the data follow the appropriate rules for its field? 
• Data Management Best Practices
  • Build a Data Management Strategy – A clear data strategy helps keep your organization on track. It’s a great way to align your team on how data will be collected, reviewed, and used to drive your business goals.
  • Improve Data Quality – Tracking, reporting, and the effectiveness of your Salesforce deployment depend on getting clean data. 
  • Import Data – Bring your existing data into Salesforce so you can include past records in your tracking and reporting. 
  • Maintain and Clean Up Data – Remove duplicates.

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