Popular Valid Analytics-Con-201 Test Topics to pass Salesforce Certified CRM Analytics and Einstein Discovery Consultant - Recommend by Many People

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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Einstein Discovery22%- Deployment & Usage
  • 1. Interpret results and recommendations
  • 2. Monitor and maintain model performance
  • 3. Embed predictions in dashboards and Lightning pages
- Model Building & Configuration
  • 1. Prepare and assess data for modeling
  • 2. Select prediction type: numeric, binary, multi-class
  • 3. Train, evaluate, and refine models
Topic 2: Security & Access12%- Sharing & Permissions
  • 1. Manage user and group access
  • 2. Configure app, dataset, and field-level security
  • 3. Implement row-level security and inheritance
Topic 3: Analytics Administration & Deployment18%- Implementation & Maintenance
  • 1. Manage versioning and deployment
  • 2. Use Dashboard Inspector and Publisher
  • 3. Monitor and optimize query performance
Topic 4: Dashboard Design & Development25%- Build & Configure Dashboards
  • 1. Create lenses, steps, and visualizations
  • 2. Use SAQL for advanced queries
  • 3. Implement bindings, filters, and interactions
- Design Principles
  • 1. Design for mobile and Lightning compatibility
  • 2. Apply best practices for usability and performance
Topic 5: Data Layer23%- Data Sources & Integration
  • 1. Define datasets and map Salesforce objects
  • 2. Use Dataflows and Recipes for transformation
  • 3. Extract, load, and prepare data using Data Manager
- Data Management & Optimization
  • 1. Implement data modeling and relationships
  • 2. Optimize dataset performance
  • 3. Resolve data sync and refresh issues

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q79-Q84):

NEW QUESTION # 79
Cloud Kicks needs a CRM Analytics consultant to install the Appointment Analytics App. After installation, they realize the wrong field was picked and the app did not have access to a newly created field that should be used instead of the old one.
What is the first step the consultant should take to prevent erroneous dataset/dashboard creation?

Answer: C

Explanation:
If the wrong field is selected in the initial setup of an app or dataset, it is important to stop any data processing activities (like recipe executions) to prevent erroneous data from being loaded into datasets and dashboards. In this case, stopping the recipe from running in Data Manager is the correct first step. Once the recipe is stopped, the consultant can update the field selection or make other necessary corrections before restarting the process.
Reference: TheData Managersection in CRM Analytics outlines how recipes and data flows are managed, including how to stop or pause recipes.


NEW QUESTION # 80
A CRM Analytics consultant is asked to make sure the new sales performance dashboard is accessible in the mobile app. They have created multiple mobile layouts for the same dashboard.
How is CRM Analytics expected to perform?

Answer: B


NEW QUESTION # 81
Universal Containers (UC) uses a Microsoft Azure SQL Data Warehouse to gather information about sales reps' objectives. UC wants to use CRM Analytics to gain insights from this data and automatically load it into a CRM Analytics dataset daily. The data also needs to be transformed and merged with data from the company's org.
Which CRM Analytics user interface features should be used to complete these requirements?

Answer: B


NEW QUESTION # 82
Universal Containers has a dashboard for sales managers. They need to visualize the percentage of their opportunities in the pipeline in a Gauge chart. They want to customize the chart to keep track if they are below or beyond the target.

Which widget parameters should a consultant use?

Answer: B

Explanation:
In the scenario described, the sales managers at Universal Containers require a Gauge chart that not only shows the current percentage of opportunities in their pipeline but also indicates whether they arebelow or beyond their set targets. The appropriate widget parameters to achieve this visualization in Salesforce CRM Analytics (formerly known as Einstein Analytics) are:
* Reference Line: This parameter is crucial for defining a specific target value on the gauge chart. It visually marks a point that represents the target goal, providing an immediate visual cue as to whether the current percentage is below or above this point.
* Markers: Markers are used to represent and highlight specific values on the gauge chart. They can be utilized to emphasize the current percentage level of the pipeline, making it instantly visible how close or far the current value is from the reference line or target.
* Conditional Formatting: This feature allows the chart to change color or style based on whether the current values meet, exceed, or fall below the target. It is a critical visual tool for quickly communicating performance against targets. Conditional formatting can be set to alter the appearance of the gauge's fill color based on whether the values are above, equal to, or below the reference line, thereby providing an intuitive visual representation of performance relative to targets.
The combination of these three parameters enables a highly effective visualization for sales managers to monitor their performance against key metrics and targets directly on their dashboards. This setup is aligned with Salesforce's best practices for creating meaningful and actionable insights within CRM dashboards, ensuring that users can easily interpret and react to the data presented.
For more details on configuring these parameters, you can refer to Salesforce documentation and specific Trailhead modules that cover dashboard creation and customization:
* Wave Analytics Explorer
* Building Lenses, Dashboards, and Apps in CRM Analytics
These resources provide in-depth training and examples to help users effectively use Salesforce CRM Analytics for a wide range of data visualization needs.


NEW QUESTION # 83
The CRM Analytics consultant at Universal Containers has set data syncs and recipe runs back to back.
However, they notice that the data syncs and recipe run jobs fail repeatedly. Upon investigation, they realize the data syncs and recipes are tightly coupled which leads to too many runs being queued and eventually being canceled.
How should the consultant resolve this issue?

Answer: B


NEW QUESTION # 84
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