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| Section | Weight | Objectives |
|---|---|---|
| Administration | 16% | - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations - Understanding recipes (Data Prep) for visual ETL interface |
| Einstein Discovery | 12% | - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Analyzing model results and proposing data improvements - Monitoring and interpreting Model Cards to improve or maintain model performance - Adjusting data parameters and adding/removing data or columns to improve models |
| Analytics Dashboard Design | 13% | - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Data Layer | 24% | - Incremental sync configuration - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs - Data extraction from sources - Understanding denormalized sequential datasets |
| Security | 16% | - Implementing security settings for users, groups, and profiles - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements |
| Analytics Dashboard Implementation | 19% | - Configuring dashboards using accurate query types and widget-level parameters - Developing selection/result interactions with different query types - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Monitoring and optimizing query performance using Dashboard Inspector - Making dashboards actionable and accessible in Lightning pages - Using advanced functionality such as windowing and time series analysis in compare tables |
What is the test called that helps to identify categories that exhibit a pattern outside the overall average?
An Einstein Analytics consultant has been asked to refactor a dashboard so that it loads more quickly. After some analysis, the consultant found that most of the dashboard steps run in less than five seconds; however, the Opportunities Table takes 30 seconds to run.
How can the consultant improve the performance of this dashboard?
Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).
What is the order of filter, limit, order and offset functions in SAQL?
Filter and order can be interchanged. Offset must be after filter/order and limit must come after offset.
A client has a dataset comprised of Opportunity data and Opportunity Line level dat a. The client wants to use this dataset with Einstein Discovery to understand the Opportunity win rate.
What must be done to the dataset to achieve this goal?
What are the steps in the data exploration process?
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