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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 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 |
| Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations |
| Einstein Discovery | 12% | - 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 - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) |
| Analytics Dashboard Implementation | 19% | - Developing selection/result interactions with different query types - Making dashboards actionable and accessible in Lightning pages - Using advanced functionality such as windowing and time series analysis in compare tables - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters - Implementing delivery management strategies using versioning and/or Dashboard Publisher |
| Data Layer | 24% | - Troubleshooting failed dataflow jobs - Data extraction from sources - Data transformation and loading into Einstein datasets - Incremental sync configuration - Dataflow configuration for multi-object scenarios - Understanding denormalized sequential datasets |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
Question 1
What is an appropriate response when a client is disappointed that Einstein Discovery only detected patterns that were already known?
A. Advise them that the use case may not be accurate.
B. Recommend using a visualization tool, like Einstein Analytics, to uncover the details.
C. Highlight that Einstein reduces time to insight, which is much faster than learning from experience.
D. Remind them that the technology is only as good as the data.
Question 2
A company wants to use Einstein Analytics to analyze the trend of their sales pipeline. The company wants to accelerate the sales process. To do so, they need to know the average time an opportunity spends in each stage. This value is not pre-calculated in Salesforce, so a consultant recommends using a dataflow to calculate it.
How can a dataflow be used to calculate the average time an opportunity spends in each stage?
A. flatten Transformation with Offset Expressions to calculate the duration
B. computeExpression Transformation with SAQL Expression to calculate the duration
C. computeRelative Transformation with offset parameter to calculate the duration
D. computeRelative Transformation with SAQL Expression to calculate the duration
Question 3
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 (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);
C. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');
D. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);
Question 4
The Universal Containers company built three Einstein Discovery stories that they want to use in Salesforce to predict and maximize their revenue per customer. The stories are for every region where they have business: EMEA, AMER, and APAC.
How can a consultant help them deploy the three Einstein models to Salesforce?
A. Deploy the same model to all accounts and set the region field as an actionable variable.
B. Deploy the same model to all accounts and use an Apex trigger to call the appropriate prediction.
C. Segment the account data per region and deploy the same model to all segments.
D. Segment the account data per region and deploy the appropriate model for each segment.
Question 5
A story created has an AUC of .62.
What two things could be done to possible improve this AUC score?
Choose 2 answers
A. Segment the training set.
B. Run detect sentiment on the dataflow before creating the story.
C. Add more rows of relevant data.
D. Change the order of the story insights to descending.
Solutions:
| Question 1 Answer: D | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: B,D |
