Salesforce Consultant Tableau-CRM-and-Einstein-Discovery-Consultant Full Course Free

In a Compare Table within Tableau CRM, when you are working with formula columns, you can change both the formula itself and the name of the column. This allows you to tailor the calculations and the labeling to better fit the analysis needs and to make the table more understandable for users.

Insights categorized as 'Unrelated' in Einstein Discovery are significant because they highlight factors or variables that, while not directly related to the primary variables of interest, may still influence the outcome through broader or global effects. These insights help in understanding the comprehensive landscape of the data, acknowledging that some influencing factors might be external or less obvious yet impactful. This broader view aids in a more holistic analysis and prevents oversight of potential influences outside the immediate dataset. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_insights_unrelated.htm &type=5

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.

In Einstein Analytics (Tableau CRM), the security options include:

• Inherited Security: Utilizes the existing Salesforce security model to control access.
• Row-level Security with Security Predicates: Offers granular control by restricting data visibility at the row level based on specific criteria.
• App Level Security: Manages access at the application level within Einstein Analytics.

Field-level security is not directly configurable within Einstein Analytics; instead, it's managed through the broader Salesforce security settings, affecting data visibility before it's imported into Einstein Analytics datasets. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_overview.htm &type=5