Salesforce Updated Tableau-CRM-and-Einstein-Discovery-Consultant Exam Questions and Answers by maira

To address the need for department-specific dashboard access while utilizing common datasets with the same row-level security, the best approach is to:

• C: Create one application for each department, put common datasets in the shared app, and use roles to share applications. This setup ensures that each department has its own tailored application containing relevant dashboards while still accessing shared datasets from a central application. The use of roles facilitates controlled access based on user roles within the organization.

Outliers in a dataset can significantly impact the results of a machine learning model, including those built by Einstein Discovery.expand_more Here's why changing binning methods is often the best approach:

• Outliers and Machine Learning: Outliers are extreme values that fall far outside the typical range of data.expand_more They can distort statistical summaries and skew the model's understanding of relationships within the data.
• Binning: Binning is the process of dividing continuous numerical data into discrete groups or bins.expand_more In Einstein Discovery, binning affects how data is visualized and analyzed.
• Fixed-width Binning: With fixed-width binning, you define equal-sized intervals for grouping data points. This can help mitigate the impact of outliers by spreading their influence across a wider range, reducing their effect on individual bins and the overall model.

Why other options are less ideal:

• A. Remove the outlier rows: While removing outliers can be effective, it risks discarding potentially valuable information. It's best used only when outliers are definitively incorrect.
• B. Nothing, because the field is not the outcome variable: Even predictor variables (inputs) with outliers can negatively impact the model. Outliers distort relationships the model is trying to learn.expand_more
• D. Remove the field because it has bad data: This is too drastic unless the whole field is unreliable. Binning adjustments are often enough to address outlier issues.