The Salesforce Tableau CRM and Einstein Discovery Consultant exam preparation guide is designed to provide candidates with necessary information about the Tableau CRM and Einstein Discovery Consultant exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Salesforce Certified Tableau CRM and Einstein Discovery Consultant exam.
It is recommended for all the candidates to refer the Tableau CRM and Einstein Discovery Consultant objectives and sample questions provided in this preparation guide. The Salesforce Tableau CRM and Einstein Discovery Consultant certification is mainly targeted to the candidates who want to build their career in Salesforce Consultant domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Salesforce Tableau CRM and Einstein Discovery Consultant exam.
Salesforce Tableau CRM and Einstein Discovery Consultant Exam Summary:
| Exam Name | Salesforce Tableau CRM and Einstein Discovery Consultant |
| Exam Code | Tableau CRM and Einstein Discovery Consultant |
| Exam Price |
Registration fee: USD 200 Retake fee: USD 100 |
| Duration | 90 minutes |
| Number of Questions | 60 |
| Passing Score | 68% |
| Recommended Training / Books |
Building Lenses, Dashboards, and Apps in CRM Analytics (ANC201) Implement and Manage CRM Analytics (ANC301) |
| Schedule Exam | Kryterion Webassessor |
| Sample Questions | Salesforce Tableau CRM and Einstein Discovery Consultant Sample Questions |
| Recommended Practice | Salesforce Certified Tableau CRM and Einstein Discovery Consultant Practice Test |
Salesforce Tableau CRM and Einstein Discovery Consultant Syllabus:
| Section | Objectives | Weight |
|---|---|---|
| Data Layer |
- Given data sources, use Data Manager to extract and load the data into the Tableau CRM application to create datasets. Describe how the Salesforce platform features map to the Model-View-Controller (MVC) pattern. - Given business needs and consolidated data, implement refreshes, data sync (replication), and/or recipes to appropriately solve the basic business need. Identify the common scenarios for extending an application's capabilities using the AppExchange. - Given a situation, demonstrate knowledge of what can be accomplished with the Tableau CRM API. - Given a scenario, use Tableau CRM to design a solution that accommodates recipe limits. |
24% |
| Security |
- Given governance and Tableau CRM asset security requirements, implement necessary security settings including users, groups, and profiles. - Given row-based security requirements and security predicates, implement the appropriate dataset security settings. - Implement App sharing based on user, role, and group requirements. |
11% |
| Administration |
- Using change management strategies, manage migration from sandbox to production orgs. - Given user requirements or ease-of-use strategies, manage dataset extended metadata (XMD) by affecting labels, values, and colors. - Given a scenario, improve dashboard performance by restructuring the dataset and/or data using lenses, pages, and filters. - Given business and access requirements, enable Tableau CRM, options, and access as expected. |
9% |
| Tableau CRM Dashboard Design |
- Given a customer situation, determine and define their dashboarding needs. - Given customer requirements, create meaningful and relevant dashboards through the application of user experience (UX) design principles and Tableau CRM best practices. - Given business requirements, customize existing Tableau CRM template apps to meet the business needs. |
19% |
| Tableau CRM Dashboard Implementation |
- Given business requirements, define lens visualizations such as charts to use and dimensions and measures to display. - Given customer business requirements, develop selection and results bindings/interactions with static queries. - Given business expectations, create a regression time series. - Given customer requirements, develop dynamic calculations using compare tables. - Given business requirements that are beyond the standard user interface (UI), use Salesforce Analytics Query Language (SAQL) to build lenses, configure joins, or connect data sources. |
18% |
| Einstein Discovery Story Design |
- Given a dataset, use Einstein Discovery to prepare data for story output by accessing data and adjusting outputs. - Given initial customer expectations, analyze the story results and determine suggested improvements that can be presented to the customer. - Given derived results and insights, adjust data parameters, add/remove data, and improve story as needed. - Understand models in production and enable prediction features across Salesforce CRM and Tableau CRM. |
19% |