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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 22% | - Deployment & Usage
|
| Topic 2: Data Layer | 23% | - Data Sources & Integration
|
| Topic 3: Security & Access | 12% | - Sharing & Permissions
|
| Topic 4: Dashboard Design & Development | 25% | - Design Principles
|
| Topic 5: Analytics Administration & Deployment | 18% | - Implementation & Maintenance
|
1. Universal Containers (UC) builds three Einstein Discovery models in Salesforce to predict and maximize its revenue per customer. The modelsare for every region UC has a business: EMEA, AMER, and APAC.
How should a consultant help UC deploy the three Einstein models to Salesforce?
A) Filter the account data per region and deploy the same model to all segments.
B) Segment the account data per region and deploy the appropriate model for each segment.
C) Deploy the same mode! to all accounts and use an Apex trigger to segment the prediction.
2. The marketing team at Cloud Kicks has five dashboards in an app. Four widgets are replicas of each other in three of the dashboards.
What is the best way to maintain these widgets?
A) Create/Edit the widgets individually on each dashboard.
B) Create/Edit a lens and add each dashboard.
C) Create/Edit a component for the widgets.
3. Exhibit.
Given that the queries are using different datasets, which change should a CRM Analytics consultant make to solve this issue?
A) Use "Connect Data Sources" and create a connection to connect the two datasets.
B) Use "Connect Data Sources" and create a connection to connect the two widgets.
C) Use result binding/Interaction in the filters section of the query "Type 1".
4. A versioning feature allows CRM Analytics users to be added as Publishers and make changes separately while a 'Live' version is still being usedby other users. Once the changes are complete, the user can then set their updated version as the Live version.
Which CRM Analytics item is this leveraged for?
A) App
B) Dataset
C) goats
5. After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?
A) The model contains too many outlier values.
B) The dataset contains multiple dominant values.
C) The outcome variable may be causing data leakage.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |
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