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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics Dashboard Implementation | 19% | - Developing selection/result interactions with different query types - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Using advanced functionality such as windowing and time series analysis in compare tables - Configuring dashboards using accurate query types and widget-level parameters - Monitoring and optimizing query performance using Dashboard Inspector - Making dashboards actionable and accessible in Lightning pages |
| Topic 2: Security | 16% | - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements |
| 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: Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - 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 - Adjusting data parameters and adding/removing data or columns to improve models |
| Topic 5: Administration | 16% | - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface |
| Topic 6: Data Layer | 24% | - Data extraction from sources - Understanding denormalized sequential datasets - Incremental sync configuration - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Troubleshooting failed dataflow jobs |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
1. A company's Salesforce org has multi-currency enabled. This company's business intelligence team used Einstein Analytics to build a dataflow that creates a dataset, "OpportunityDataSet". This dataset is populated with data extracted from the standard object, Opportunity. One of the extracted fields is the standard field, Amount.
If a user explores the "OpportunityDataSet" in Einstein Analytics, in which currency will the Amount values be shown?
A) In the currency that is set on the "currency" attribute in the dataset
B) In the connected user's currency
C) In the currency that is set on the "currency" attribute in the dataflow
D) In the integration user's currency
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', seasonality=4);
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');
3. Yasmine told Danielle she's interested in using the artificial intelligence provided by Einstein Discovery to enhance analysis Mosaic probably wouldn't have considered through their own means. How can Danielle use Einstein Analytics to load data into Einstein Discovery?
A) Data loaded into Einstein Analytics as datasets can then be used in Einstein Discovery without a separate load process.
B) Danielle needs a permission set containing the Download Analytics Data permission, then she can use the Export node to load data into Einstein Discovery.
C) Danielle needs a permission set containing the Upload External Data to Analytics permission, then she can log into Einstein Discovery and import the data from there.
D) It's not possible. Danielle will need to log into Einstein Discovery and import the data from there.
4. A story created has an AUC of .62.
What two things could be done to possible improve this AUC score?
Choose 2 answers
A) Change the order of the story insights to descending.
B) Run detect sentiment on the dataflow before creating the story.
C) Add more rows of relevant data.
D) Segment the training set.
5. Why is it important to prepare the data in a CSV file before you bring it into Analytics?
A) To add currency symbols to numeric fields
B) To remove column headers
C) To make sure Analytics can set the field type correctly
D) To make sure the content is interesting
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A,B | Question # 5 Answer: C |




