Einstein Prediction Builder is probably one of the easiest Accredited Professional Exam. Even if you are new to Salesforce you can go through the curriculum and pass the exam with flying colors!
1. About the Einstein Prediction Builder Accredited Professional Exam
The Einstein Prediction Builder Accredited Professional exam is intended for individuals who have the knowledge, skills, and experience with data ingestion processes, security and access implementations.
content Content 20 multiple-choice/multiple-select questions
duration Time allotted 30 minutes
bulls eye Passing score 80% (16 out of 20 questions)
price tag Exam Fee USD 150 plus applicable taxes
retry Retake Fee USD 75 plus applicable taxes
options Prerequisite None
2. Exam Outline
| Topics | Weighting |
| Einstein Prediction Builder Basics● Describe what you can do with Einstein Prediction Builder.● Define common terminology associated with Einstein. | 65% |
| Einstein Prediction Builder Advanced Topics● Create custom fields for building and enriching a prediction.● Build a prediction to determine the likelihood that restaurant customers won’t show up for their reservations.●Create a list view showing your riskiest reservations. | 35% |
3. Einstein Prediction Builder Accredited Professional Exam Study Course
Accredited Professional Exam curriculum is available on Salesforce Partner Learning Camp. Please refer to Accredited Professional Partner Community Page for details.
4. Important Topics for Einstein Prediction Builder Accredited Professional Exam
- Artificial Intelligence for Business
- Four main ingredients that are part of any good AI platform:
- Yes-and-no predictions
- Numeric predictions
- Classifications
- Recommendations
- Three steps for starting right with AI
- Decide what to predict
- Get historical data in order
- Turn predictions to action
- Send Time Optimization helps predicts the best time to send a communication for the highest response rate, specific to each person
- Einstein Engagement Frequency predicts the right number of communications to send without going overboard
- Salesforce Einstein Basics
- Einstein is Your Smart CRM Assistant
- 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.
- What Makes Einstein Different?
- Data in Salesforce
- Tailored Predictions
- It Lives on the Salesforce Platform
- Einstein Out-Of-The-Box Applications
- Sales Cloud Einstein
- Service Cloud Einstein
- Marketing Cloud Einstein
- Commerce Cloud Einstein
- Einstein Platform products:
- Einstein Bots
- Einstein Voice
- Einstein Prediction Builder
- Einstein Next Best Action
- Einstein Discovery
- Einstein Vision and Language
- Responsible Creation of Artificial Intelligence
- Machine Learning (ML): technique that allows a computer to “learn” from examples without having been explicitly programmed with step-by-step instructions
- Artificial intelligence(AI): teach computers to perform complex tasks and behave in ways that give the appearance of human agency
- AI can be composed of algorithms
- An algorithm is a process or set of rules that a computer can execute
- AI algorithms can learn from data
- When you have trained an algorithm with training data, you have a model.
- The data used to train a model is called a training dataset.
- The data used to test how well a model is performing is call test dataset.
- Measurement or Data Set Bias
- Association Bias: Data are labeled according to stereotypes
- Confirmation Bias: Labels data based on preconceived ideas
- Automation Bias: Imposes a system’s values on others
- Societal Bias: Reproduces the results of past prejudice toward historically marginalized groups
- Survival or Survivorship Bias: Algorithm focuses on the results of those were selected, or who survived a certain process, at the expense of those who were excluded
- Interaction Bias: Intentionally try to influence AI systems and create biased results
- How Does Bias Enter the System?
- Assumptions: make assumptions about what they should build, who they should build for, and how it should work, including what kind of data to collect from whom
- Training Data: AI models need training data, and it’s easy to introduce bias with the dataset
- Model: the factors that you use in the model, such as race, gender, or age, can result in recommendations or predictions that are biased against certain groups defined by those characteristics
- Human Intervention (or Lack Thereof): Editing training data directly impacts how the model behaves, and can either add or remove bias
- How to Manage Risks of Bias?
- Conduct Premortems
- Identify Excluded or Overrepresented Factors in Your Dataset
- Regularly Evaluate Your Training Data
- Filter-Based Predictions with Einstein Prediction Builder
- Einstein Prediction Builder process.
- Step 1—Define Your Use Case.
- Step 2—Identify the Data That Supports Your Use Case.
- Step 3—Create Your Prediction.
- Step 4—Review, Iterate, Enable, and Monitor Your Prediction.
- Step 5—Build the Prediction into Your Business Workflow.
- Step 6—Measure Success and Iterate.
- Einstein Prediction Builder Terminology
- Dataset: The overall set of data; the set of records on the object you’re predicting (for example, the records from the Invoice object
- Segment: A subset of your dataset that includes filtering in certain records, defined by filter conditions (for example, adding a filter to include customers who do not have automatic payments).
- Example set(Training Set): Data from the past that Einstein learns from and uses to build the prediction.
- Example set: “Yes” Examples: Data examples that have a “yes” value for what you are predicting (for example, a customer paying their invoice late). This is required for a yes/no prediction but not a numerical prediction.
- Example set: “No” Examples: Data examples that have a “no” value for what you are predicting (for example, a customer paying their invoice on time). This is required for a yes/no prediction but not a numerical prediction.
- Prediction set: The set of records for which Einstein predicts values.
- Score (result): Predicted value on a record in your dataset. The score for a yes/no prediction, is the probability of your prediction being true. The score for a numerical prediction, is the predicted number (that is, the predicted price of a house). It appears in a custom field that Einstein adds to the records.
- Einstein Prediction Builder Guided Setup Steps
Einstein Prediction Builder Guided Setup Steps
- Outcome Types
- Yes/No
- Numeric
- Einstein Prediction Builder can make predictions for these types of fields.
- Checkbox
- Specially constructed formula fields
- Numeric
- Einstein Prediction Builder Limits
| Description | Limit |
| Maximum Fields to be included for prediction’s outcome | 1000 |
| Minimum number of records for overall dataset (or segment if dataset is segmented) | 400 |
| Minimum number of records for example records (example set) | 400 |
| Minimum number of records for true and false values (binary fields only) | 100 per value |
| Minimum number of records to predict | 1 |
Einstein Prediction Builder Limits
- You can view prediction quality on Overview tab of the Scorecard
Einstein Prediction Builder Scorecard
