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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics Dashboard Implementation | 19% | - Monitoring and optimizing query performance using Dashboard Inspector - 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 - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Configuring dashboards using accurate query types and widget-level parameters |
| Topic 2: Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Incremental sync configuration - Data extraction from sources - Troubleshooting failed dataflow jobs - Understanding denormalized sequential datasets |
| Topic 3: Analytics Dashboard Design | 13% | - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles |
| Topic 4: 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 |
| Topic 5: Security | 16% | - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates |
| Topic 6: Einstein Discovery | 12% | - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Analyzing model results and proposing data improvements - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Adjusting data parameters and adding/removing data or columns to improve models - Monitoring and interpreting Model Cards to improve or maintain model performance |
1. Trending data limits: Maximum number of trended datasets per user and rows per snapshot:
A) 5 per user, 200,000 per snapshot
B) 5 per user, 100,000 per snapshot
C) 10 per user, 200,000 per snapshot
D) 10 per user, 100,000 per snapshot
2. After the initial creation of a story, the first story insight explains 93% of the variation of the outcome variable. This is unusual high?
What is the most likely multiple for this?
A) The outcome variable is causing data leakage.
B) The dataset contains multiple dominant values.
C) The dataset contains too many rows.
D) The dataset used in the story suffers from too many outlier values.
3. Exhibit:
A company wants to create a timeline chart to visualize the evolution of their Closed Won Opportunities. What are the required parameters to build a lens that displays output similar to the image shown?
A) 1 measure, 0 groupings if trellis is disabled, or 0-2 groupings if trellis is enabled
B) 1 measure, 1 grouping by a date field, and either 0-1 groupings by a dimension if trellis is disabled, or 0-2 groupings if trellis is enabled
C) 1 measure, 1-2 groupings if trellis is disabled, or 1-4 groupings if trellis is enabled
D) 2 measures, 0-2 groupings if trellis is disabled, or 0-4 groupings if trellis is enabled
4. How many external files can be uploaded to Einstein Analytics on rolling 24 hour basis?
A) 50 files Per dataset
B) 300 files per dataset
C) 500 files per dataset
D) 100 files per dataset
5. Philip adds a recently created Seed Bank Orders dataset to an Einstein Analytics app for which the Mosaic Seed Bank project team has the Viewer app role. How much data in the Seed Bank Orders dataset can a project team member see?
A) Only rows designated by the App Manager
B) All rows and fields in the datasets
C) None of the data
D) Only rows designated by the Salesforce administrator
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |
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