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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Layer | 23% | - Data Management & Optimization
|
| Topic 2: Security & Access | 12% | - Sharing & Permissions
|
| Topic 3: Einstein Discovery | 22% | - Deployment & Usage
|
| Topic 4: Analytics Administration & Deployment | 18% | - Implementation & Maintenance
|
| Topic 5: Dashboard Design & Development | 25% | - Design Principles
|
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NEW QUESTION # 90
A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.
What should the consultant do?
Answer: C
Explanation:
Reference:CRM Analytics Recipes and Predict Missing Values
NEW QUESTION # 91
Universal Containers has a well-defined role hierarchy in Salesforce where everyone is assigned to an appropriate node. The accounts within their instance are categorized by their demography.
An individual sales rep should be able to view all accounts that they own. In addition, sales reps should be able to see any accounts where the value of the account demography matches the demography defined on their user record. A user could have more than one demography defined on their user record.
To meet this requirement, the CRM Analytics consultant has set up a security predicate of the existing
'Account' dataset as follows:
This, however, does not seem to be working as expected.
What is causing the issue?
Answer: A
Explanation:
The issue with the security predicate not functioning as expected likely stems from a permissions issue related to the custom field Demographic__c on the User object. Here's a detailed explanation:
* Field-Level Security: If the sales reps do not have access to the Demographic__c field, the security predicate which references this field cannot execute properly as the system cannot evaluate the predicate without accessing the field.
* Permission Settings: Ensuring that the sales reps have the necessary permissions to view and use the Demographic__c field is crucial for the security predicate to function correctly.
* Data Visibility: The security model in CRM Analytics relies heavily on the underlying data permissions in Salesforce. If these permissions are not correctly configured, the expected data visibility through CRM Analytics will not be achieved.
NEW QUESTION # 92
A dashboard designer at Cloud Kicks creates a dashboard in CRM Analytics. The designer notices fields display on the dashboard with their APIlabels, such as "AccountId.Industry", and wants to change this behavior.
The designer also notices that the fields and their order appear to randomly change when a values table is created.
What should the CRM Analytics consultant explain to help the designer?
Answer: B
Explanation:
For the scenario at Cloud Kicks where fields display with their API labels and the fields in a values table seem to change order randomly, the correct approach is to modify these settings in the dataset explorer within CRM Analytics. This allows for a more intuitive display and control over how data is presented in dashboards.
Here's how these adjustments help:
* Modifying Field Labels: Changing the field labels from their API names to more user-friendly names enhances readability and user experience. This can be done directly in the dataset explorer, which affects how fields appear across all dashboards utilizing that dataset.
* Controlling Field Order: The order of fields in a values table can seem random if not explicitly set. By using the dataset explorer, a designer can specify the order in which fields appear, which then reflects consistently in the dashboard's values table.
This functionality is part of CRM Analytics' aim to provide flexible and customizable data visualization tools.
Training on these features is available through various Salesforce Trailhead modules that discuss dashboard and dataset customization techniques, providing practical insights and guided tutorials to enhance dashboard design and user interaction.
Both these explanations are consistent with best practices as outlined in Salesforce's CRM Analytics documentation and the Trailhead educational content, ensuring that users are well-equipped to leverage the full capabilities of CRM Analytics for effective data management and presentation.
NEW QUESTION # 93
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 # 94
consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.
Which action should the consultant take?
Answer: B
Explanation:
Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here's the rationale:
* Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired.
* Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions.
* Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It's about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making.
By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client's business strategies and operational realities.
NEW QUESTION # 95
......
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