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| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Troubleshooting failed dataflow jobs - Understanding denormalized sequential datasets - Data extraction from sources - Incremental sync configuration - Data transformation and loading into Einstein datasets |
| Security | 16% | - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements - Implementing security settings for users, groups, and profiles |
| Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Monitoring and interpreting Model Cards to improve or maintain model performance - Analyzing model results and proposing data improvements - Adjusting data parameters and adding/removing data or columns to improve models - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) |
| Analytics Dashboard Implementation | 19% | - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Using advanced functionality such as windowing and time series analysis in compare tables - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Configuring dashboards using accurate query types and widget-level parameters - Monitoring and optimizing query performance using Dashboard Inspector |
| Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce - Understanding recipes (Data Prep) for visual ETL interface |
| Analytics Dashboard Design | 13% | - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Creating dashboards following CRM Analytics best practices and UX design principles |
1. Upper and lower limits on rows in discovery.
A) 500 row minimum, 25,000,000 max
B) 3000 row minimum, 20,000,000 max
C) 00 row minimum, 20,000,000 max
D) 1000 row minimum, 15,000,000 max
2. Creating an Einstein Discovery story involves:
A) Uploading a Microsoft PowerPoint presentation
B) Copying text from company reports and pasting into Einstein Discovery
C) Selecting a Dataset in Einstein Analytics, then clicking Create Story
D) Entering notes about the data
3. Why are insights in the Unrelated category important?
A) They explain how interaction terms affect the outcome.
B) They explain everything about the outcome.
C) They aren't important. Hence the name, Unrelated.
D) The outcome might not have much to do with related variables. It might have more to do with global effects.
4. What happens if the ui.json file is empty?
A) You create a text-only version of an app.
B) The template will not include a configuration wizard.
C) B and D
D) The app includes all possible dashboards by default.
E) The dataflow fails.
5. The Vice President of Sales wants to create a "Pipeline Trending" dashboard in Einstein Analytics that displays specific colors depending on the values of the fields.
Which two techniques can a consultant use to help address this requirement? Choose 2 answers
A) Use a compute Expression to calculate a color field and assign it to each field value.
B) Use the "fields" menu when exploring the dataset and assign colors to each field value.
C) Use SAQL queries to calculate a color field and assign it to each field value.
D) Use an XMD file and assign colors to each field value.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: B,D |
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