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| Section | Objectives |
|---|---|
| Dashboard and App Development | - Lens creation and dashboard design - Interactive filters and user experience optimization |
| Data Integration and Connectivity | - Data synchronization and refresh strategies - Connecting external data sources |
| Data Management and Preparation | - Data transformation and dataflows - Dataset creation and configuration in CRM Analytics |
| Security and Access | - Row-level security and sharing rules - App and dataset permissions |
| Einstein Discovery | - Model building and predictions - Insights interpretation and recommendations |
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NEW QUESTION # 105
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.
NEW QUESTION # 106
A new picklist value was added for the Category field on the Account object. This field is already added as part of the Account object data sync and the respective recipe that uses this field.
The CRM Analytics team reports that when they start the recipe it runs successfully with no errors or warnings, but theyare unable to See this new valueson their existing dashboards.
What is the oridi4 of this issue?
Answer: C
NEW QUESTION # 107
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
Answer: C
Explanation:
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.
NEW QUESTION # 108
A company realizes it has a lot of rich information around its cases, but unfortunately, most of this isunstructured/textual data. Thecompany is exploring how to include some of this information in its case prioritization.
Which option within CRM Analytics should a consultant leverage?
Answer: B
Explanation:
For a company with a wealth of unstructured textual data in their cases, the "Detect Sentiment" transformation within CRM Analytics Recipes is a crucial tool. This transformation analyzes the sentiment of the text data- whether it's positive, neutral, or negative-and this insight can be highly valuable in case prioritization processes. Here's why this transformation is useful:
* Insight into Customer Sentiments: By detecting sentiment, the company can prioritize cases based on the urgency and emotional tone expressed in the text, which might indicate customer dissatisfaction or urgency.
* Automation and Efficiency: Automatically categorizing cases based on sentiment can streamline workflows and ensure that critical cases are handled promptly.
* Enhanced Customer Service: Responding to negative sentiments swiftly can improve customer satisfaction and potentially mitigate issues before they escalate.
NEW QUESTION # 109
The CRM Analytics consultant at Universal Containers notices that some users have access to sensitive data and dashboards they should not have access to in the Manager's app.
How should the consultant fix the problem?
Answer: B
Explanation:
Reference:Managing Data Access and Sharing in CRM Analytics
NEW QUESTION # 110
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