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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 12% | - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Analyzing model results and proposing data improvements - Monitoring and interpreting Model Cards to improve or maintain model performance - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Adjusting data parameters and adding/removing data or columns to improve models |
| Topic 2: Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce |
| Topic 3: Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
| Topic 4: Analytics Dashboard Implementation | 19% | - Using advanced functionality such as windowing and time series analysis in compare tables - Configuring dashboards using accurate query types and widget-level parameters - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Monitoring and optimizing query performance using Dashboard Inspector |
| Topic 5: Security | 16% | - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles |
| Topic 6: Data Layer | 24% | - Data extraction from sources - Troubleshooting failed dataflow jobs - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Incremental sync configuration - Understanding denormalized sequential datasets |
1. What's the magic of faceting?
A) It creates a chart that shows all the faces of your data in a diamond chart.
B) It turns the dataflow on and off.
C) It shifts the time period of the chart you're viewing.
D) It binds charts together so that selecting something in one chart filters other charts by that choice.
2. The client is trying to create a SAQL step to predict sales in each sales region. They cannot get the query to return any results, but have identified that the error is in the time series statement. They have asked an Einstein Consultant to review the following query and fix any errors.
Which timeseries statement will fix the query'
A) q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');
B) q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');
C) q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);
D) q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);
3. Why is it important to prepare the data in a CSV file before you bring it into Analytics?
A) To make sure Analytics can set the field type correctly
B) To remove column headers
C) To add currency symbols to numeric fields
D) To make sure the content is interesting
4. Refer to the graphic.
Which conclusion can be made regarding the strength of the model shown?
A) The model is pretty good; the accuracy rating of .7597 means we can predict both wins and losses at a fairly high rate.
B) The model is very weak and doesn't provide useful predictions due to the low threshold.
C) The model is very strong. A GINI coefficient of .535 shows that this model is very effective.
D) The strength of the model cannot be determined with the metrics shown.
5. What's the best way to describe the template-info.json file?
A) It's automatically populated every time you create an app from the template.
B) It's a read-only file with all the user documentation for a template.
C) It's the boss of the template with all the information needed to create the app.
D) It makes sure that the app includes all the right Salesforce data.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: C |
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