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| Section | Objectives |
|---|---|
| Security and Access | - Row-level security and sharing rules - App and dataset permissions |
| 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 | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
| Einstein Discovery | - Model building and predictions - Insights interpretation and recommendations |
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NEW QUESTION # 74
Universal Containers plans to upload target data from an external tool to CRM Analytics so it can calculate the sales team target attainments.
The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales rep, the target amount, and the month of the target. For each sales rep, the file contains a target for every month of the current year as well as all previous years.
Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?
Answer: C
Explanation:
For uploading target data that changes on a monthly basis and includes historical data (previous years' targets), the appropriate operation is "Overwrite." This ensures that each time the CSV file is uploaded, the existing data in the dataset is replaced with the new data. This is critical because the target data includes both current and historical data, and using "Overwrite" will update the entire dataset while maintaining historical accuracy.
"Append" would add new data without replacing the old records, leading to duplication, and "Update" is not suitable for completely replacing data in this context.
NEW QUESTION # 75
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: A
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 # 76
A CRM Analytics consultant is asked to make changes to the current sales dashboard at Cloud Kicks. The dashboard is crucial to track the daily sales performance of the company and needs to be available for other users while the consultant works on the changes.
How should the consultant proceed to update the dashboard?
Answer: A
NEW QUESTION # 77
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?
Answer: B
NEW QUESTION # 78
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
NEW QUESTION # 79
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