Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Questions and Answers

To enhance the performance of data synchronization in Tableau CRM, after removing unused objects and fields, the next logical step is to evaluate the connection mode for each connected object. This involves analyzing whether the current connection settings (e.g., full or incremental sync) are optimized based on the data volume and update frequency. Adjusting these settings can significantly improve sync performance by reducing unnecessary data transfer and processing.

When clients express disappointment that Einstein Discovery only detected known patterns, an appropriate response is to highlight the efficiency benefits, specifically how Einstein significantly reduces the time to insight compared to traditional methods. This emphasizes the value of using Einstein Discovery to quickly validate known patterns and potentially explore nuances within those patterns more swiftly than through manual analysis.

From the description of the tooltip and the insight provided:

A is correct because it specifically compares the average daily sales in San Francisco to a global average, showing a significant difference.

A Bar chart is best suited to track the progress of sales revenue that is realized versus the target. Bar charts are effective for comparing multiple categories of data, such as actual versus target sales, side by side. This visual comparison makes it clear to see how close the realized sales are to the targets, which is crucial for quick assessment and decision-making in sales performance monitoring.

In Einstein Analytics, the "sfdcDigest" node is used in a dataflow to extract data from a registered dataset. This node is specifically designed to query and ingest data from Salesforce objects and datasets into the dataflow for further transformations and analyses.

In Einstein Discovery stories, insights show how different variables, or combinations of variables, explain the variation of the outcome variable. The outcome variable is the target variable whose behavior and influencing factors are being analyzed and predicted by the model. Insights in the story will help understand how each explanatory variable and their interactions impact the outcome, which is central to predictive analytics and machine learning in Einstein Discovery. Reference: Salesforce Help - Einstein Discovery Insights

When you configure the story, you tell Einstein Discovery to maximize/minimize the variable. The variable is 'outcome variable' in your story

XMD stands for Extended Metadata. This component in Salesforce Tableau CRM allows users to customize datasets and dashboards further. With XMD, users can define label overrides, specify numeric formats, set up color coding, and more, enhancing the visualization and interpretation of data within the analytics environment.

When developing a mobile layout for an Einstein Analytics dashboard, the consultant should consider:

A: If no layouts are eligible for the mobile device, the first defined layout is used. This ensures that there is always a fallback layout if none of the other defined layouts meet the device properties.

C: If more than one layout is eligible, the one with the most device properties set is used. If there is a tie, the most recently defined layout is used. This helps in choosing the most optimized layout for the device, enhancing user experience.

D: A layout for mobile is eligible for use when the device meets all the device properties set in the Layout panel. This consideration is crucial for ensuring that the layout displayed is suitable for the device’s capabilities and screen size.

For Discovery in Salesforce Tableau CRM, the limits on the number of columns (variables) are set at a minimum of 2 and a maximum of 50. These boundaries ensure that the Discovery models are both manageable and robust enough to generate meaningful insights without overwhelming the computational resources.

Einstein Discovery presents insights based on their importance in explaining the variation in the outcome variable, in descending order. This approach prioritizes insights that have the most significant impact on the outcome, helping users to focus on the most influential factors first. By sorting insights in this manner, Einstein Discovery ensures that users can quickly identify and address the key drivers of their business metrics, facilitating more effective decision-making based on the most critical data points. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_story_overview.htm & type=5

The Chi-square test in statistics is used to identify whether there is a significant association between categorical variables. It is especially useful in scenarios like in Tableau CRM where one might want to determine if the differences between observed and expected frequencies in one or more categories are just random variations or are statistically significant. This test helps in identifying categories that exhibit a pattern that deviates from the overall average, making it a valuable tool for data analysis in categorical data sets. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_statistical_tests.htm & type=5

In Salesforce Analytics Query Language (SAQL), the order of functions plays a crucial role in how data is processed. The 'filter' and 'order' functions can be used interchangeably depending on the specific needs of the query. However, 'offset' should always follow either 'filter' or 'order', and 'limit' must be specified after 'offset'. This order ensures that the query first narrows down the data set, then offsets into this subset, and finally limits the number of records returned.

Einstein Discovery in Salesforce functions like having a personal data scientist on staff. It utilizes advanced machine learning algorithms to analyze large volumes of data, identify patterns, and provide predictive insights and recommendations. This tool simplifies complex data analysis tasks and enables users to make informed decisions without the need for deep statistical knowledge or expertise in data science. Reference: Salesforce Einstein Discovery Overview

To display boolean fields in a dataset as "Yes" in green for true and "No" in red for false across all dashboards:

A: Download the XMD for the dataset and change the values and color for the fields and then upload to the dataset. Modifying the Extended Metadata (XMD) allows for comprehensive customization of how data values are displayed across all views that utilize the dataset.

D: In the explorer, select the boolean fields, then create a derived dimension that references the boolean fields, and then modify the values and colors of the derived dimension. This approach allows for custom display settings to be applied directly within the explorer tool, tailoring the presentation of boolean values on a per-dashboard basis.

To address the performance issue with data sync (replication) caused by unnecessary data being synced, the consultant should:

D: Update the existing data sync (replication) process by removing unused fields with a filter to extract relevant data. This approach minimizes the data volume being synchronized by focusing only on relevant fields and records, thereby improving the performance of the data sync processes.

In the context of Einstein Analytics, when creating a template app from an existing app, the JSON file that manages the template metadata and all other template elements is: D. template-info.json. This file contains essential metadata about the template, including definitions of the template's properties and behaviors, facilitating the reuse and customization of the app as a template. This file is crucial for managing and distributing template apps within Einstein Analytics. Reference: Salesforce Documentation on Analytics Templates

To delete activities in Dataset A related to users in Dataset B, the consultant can use a combination of dataflow transformations: "augment" and "filter." First, augment Dataset A with Dataset B using "userID" to combine the datasets based on user association. Then, apply a filter to retain only those records in Dataset A that do not have a corresponding entry in Dataset B.

Save changes to existing dashboards, lenses, and datasets in the app (saving dashboards requires the appropriate permission set license and permission) available in Editor and Manager*##**##*

In Salesforce Tableau CRM, dashboards can be shared with others within your organization using the "Share" icon located on the dashboard itself. This feature allows users to:

Set up specific sharing options to control who can view or edit the dashboard.

Post a screenshot of the dashboard directly to Chatter to facilitate discussion or collaboration.

Obtain a URL that can be shared with other users who have the necessary permissions to access the dashboard.

Download a screenshot of the dashboard for use in presentations or reports outside of Salesforce.

The "Progressive Disclosure" design principle in dashboard design means: A. Only provide the user with the level of detail they need to see, with the option to drill down deeper into more details. This approach helps to keep the user interface simple and clean, avoiding information overload while still offering more detailed data as needed. This principle ensures that dashboards are user-friendly and efficient, focusing on user engagement and information relevance. Reference: Salesforce Documentation on Progressive Disclosure

Each prebuilt Analytics app has its own PSL. When you set up permissions for those apps, select the right PSL. For Service Analytics, select "Service Analytics Apps." For Event Monitoring, select "Event Monitoring Analytics Apps," and so on.

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 Le arning: 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 continuo us 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 m itigate 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 ris ks 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. Outlie rs 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 Discovery, it is not necessary to remove all potential collinear fields before executing the build story because:

B: No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting. Einstein Discovery includes mechanisms such as ridge regression to manage collinearity, which helps prevent the model from over-fitting even when collinear variables are present.

In Salesforce Tableau CRM (Einstein Discovery), one of the straightforward ways to get data into Einstein Discovery for analysis is by using an Einstein Analytics dataset. These datasets can be directly used to create stories in Einstein Discovery, allowing for deep analysis and predictive modeling based on the data already collected and structured within the Salesforce environment. This method leverages the integrated nature of Salesforce products to provide a cohesive and efficient analytics experience. Reference: Salesforce Help - Data Preparation for Einstein Discovery

https://trailhead.salesforce.com/content/learn/modules/wave_exploration_smart_data_discovery_basics/wave_get_smart_data_discovery_and_data

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

The "Unexplained Bar" in the "Why it Happened" chart of Einstein Discovery represents the difference between the predicted outcome and the observed outcome. This metric is essential as it highlights the variance that the model's explanatory factors do not account for, providing insight into the limitations of the current model. This understanding aids in interpreting the accuracy and reliability of the model predictions.

For a dashboard that needs to visualize Closed Won opportunities by different time groupings (month, quarter, year) using a single chart, the appropriate action is: D. Use selection binding to update the grouping in the chart. This method allows the chart to dynamically change the grouping based on user selection, effectively switching between month, quarter, or year views within the same visual representation. Reference: Salesforce Documentation on Dynamic Dashboards

The Event Monitoring Analytics app in Salesforce is specifically designed to analyze Event Log File data. This app provides insights into user behavior and system performance, helping organizations understand how their Salesforce environments are being used and how they can be optimized. The app includes prebuilt dashboards and metrics that focus on this specific type of data.

In Tableau CRM, a Compare Table allows you to segment data into different groupings for comparison. You can have up to four groupings in a Compare Table. This feature enables users to analyze and visualize data by comparing different segments side by side within the same table, facilitating a clearer understanding of how various groups perform against each other across multiple metrics. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboard_widgets_compare_table.htm & type=5

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The appropriate action when Einstein Discovery surfaces an unexpected pattern is to determine if the pattern is a data issue or a new insight. This approach involves verifying the integrity and accuracy of the data, and understanding whether the pattern represents a meaningful insight into the business operations or an anomaly caused by data issues.

To build a table in a Sales Manager dashboard and include a summary as the first line in a compare table:

A: Use the Show Summary option in the compare table. This feature automatically calculates and displays a summary row at the top of the compare table, providing an aggregated view of the data points included in the table.

In Tableau CRM, the default number of rows displayed in a compare table is 1,000. This setting provides a balance between detailed data exploration and system performance, ensuring users can analyze significant amounts of data without overwhelming the interface or processing capabilities.

Salesforce Tableau CRM (formerly Einstein Analytics) provides specific prebuilt permission sets that allow various levels of access and capabilities within the platform. The two prebuilt permission sets that are relevant here are:

Einstein Analytics Platform Admin: This permission set grants administrative privileges within Einstein Analytics, allowing users to manage users, permissions, and overall platform settings.

Einstein Analytics Platform User: This permission set enables users to access and use Einstein Analytics for data exploration and dashboard creation, but without the administrative privileges.

These permission sets are designed to simplify user management by providing role-based access controls that can be easily assigned to different users depending on their needs within the platform. Reference: Salesforce Documentation - Einstein Analytics Permission Sets

When discrepancies arise between the aggregate sum in a dataset and manual calculations in Salesforce, common issues include: C. Differences involving formula fields. Formula fields in Salesforce are calculated dynamically and may not always be calculated during data sync if they depend on other fields that aren't triggered by record updates. D. Differences involving trigger-updated fields. Fields that are updated by triggers might not have their latest values captured if the triggers were executed after the data was replicated. These scenarios can cause inconsistencies in data reflected in Einstein Analytics compared to what is stored directly in Salesforce. Reference: Salesforce Documentation on Data Sync and Replication

When deploying models to an existing prediction in Tableau CRM, it is important to be aware of the limits imposed by the platform. Specifically, there is a limit of 10 models under a single Prediction Definition. This limit helps manage the complexity and ensures that the predictions remain manageable and efficient.

Before releasing an updated dashboard to production, the best practice is to remove redundant steps to ensure dashboard performance. This optimization helps in minimizing the load time and resource consumption, particularly important after iterative modifications such as adding or removing charts.

To properly analyze the Opportunity win rate using Einstein Discovery, it's crucial to:

D: Change the grain by removing the line level detail as it will skew the win rate. This modification ensures that the analysis focuses on the opportunities as a whole rather than the potentially misleading details of individual line items, providing a more accurate view of the overall win rate.

To ensure a user can view dashboards within a designated app without the ability to create and save lenses, the correct action is to assign the Viewer role to the user. This role allows the user to access and interact with the dashboards and apps but restricts the ability to modify, create, or save anything within the app, aligning perfectly with the requirement specified.

In the context of Tableau CRM (formerly Einstein Analytics), a lens/exploration is composed of two main parts: Visualization and Grouping. Visualization refers to how the data is presented visually, such as charts or tables. Grouping involves how data fields are categorized or segmented for analysis, allowing users to drill down into specifics or see summaries based on certain criteria.

To increase adoption among account managers who need comprehensive account overviews, embedding a dashboard onto the account record page with filters that dynamically adjust to show relevant data about active cases, opportunities, and product whitespace is most effective. Here’s how to implement this:

Design a comprehensive dashboard in Tableau CRM that includes all the necessary lenses and widgets to show key details such as active cases, opportunities, and potential product sales opportunities.

Ensure the dashboard can dynamically filter data based on the account being viewed. This typically involves using dynamic dashboard filters linked to the account record page.

Embed the dashboard onto the Salesforce account record page. This integration allows account managers to have all the necessary information directly within the context of the account they are viewing, enhancing usability and relevance.

This approach promotes better user engagement by providing relevant data directly within the user's workflow.

When you disable Analytics in Salesforce, the system does not automatically remove user permissions from the permission sets. These permissions remain intact and are not affected by the disabling of the service. Consequently, if you re-enable Analytics at a later time, there is no need to redefine these permission sets as the original configurations remain in place. This behavior ensures that administrators can temporarily disable and re-enable Analytics without redoing the setup for user access, making it efficient to manage without risking data security or access errors. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_enable_features.htm & type=5

https://blog.bessereau.eu/assets/pdfs/bi_admin_guide_setup.pdf

QUESTION NO: 131

What do you have to assign to users before they can access Analytics?

A. Analytics permission set license (PSL)

B. Permission set with at least one Analytics user permission

C. Username and password

D. A and B

E. B and C

Answer: D

Before users can access Tableau CRM (Einstein Analytics), they must be assigned specific permissions. These include: A. An Analytics permission set license (PSL), which grants the user the license to use Analytics. B. A permission set configured with Analytics user permissions, which detail what aspects of Analytics the user can access. This setup ensures that both the licensing and access rights are correctly aligned to user roles and needs within the organization. It is essential to configure both aspects to enable functional and compliant use of the platform. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security.htm & type=5

To determine whether a subscription is new or a renewal when working with Closed Won Opportunity records in Salesforce that do not explicitly specify this information, the appropriate data transformation is ComputeExpression. This transformation allows you to create a new field or modify existing ones based on a specified expression. For instance, the consultant can use logic comparing the account's first purchase date to the opportunity close date to ascertain whether the subscription is a renewal.

In Tableau CRM, each run of a recipe counts towards the 24-hour rolling recipe run limit, regardless of the duration of each recipe. Therefore, if all three recipes (myRecipeOne, myRecipeTwo, and myRecipeThree) are executed, all three count towards the limit. This is crucial for managing system resources and ensuring optimal performance of the analytics environment.

To analyze data from a CSV file in Einstein Discovery, the correct procedure involves importing the CSV file into an Einstein Analytics dataset and subsequently creating a story from this dataset. This method allows you to fully leverage Einstein Discovery's capabilities to analyze and derive insights from the data presented in the CSV file.

The best practice for handling user roles and permissions in Einstein Analytics is to create and assign Salesforce permission sets tailored to the distinct types of user roles. In this scenario, the consultant should recommend creating permission sets for:

Dataset and dashboard builders (3 users),

Dashboard creators (15 users),

Dashboard viewers (982 users).

This approach allows for fine-grained control over access rights and ensures that each user has the appropriate level of permissions based on their responsibilities. Salesforce permission sets are a flexible and scalable way to manage access and can be tailored to the specific needs of any organization.

The REST API URL where a consultant can find current template apps in Salesforce Tableau CRM (formerly Einstein Analytics) is "/services/data/vXX.X/wave/templates". This endpoint is specifically designed for interacting with template apps, allowing consultants to extend, customize, or retrieve information about available templates.

In Salesforce Analytics, the basic actions within a query that are fundamental to building meaningful visualizations and data summaries include:

A. Aggregate by a measure: This involves summarizing data using statistical measures like sum, average, maximum, etc., which help in understanding the distribution and trends of data.

C. Filter to narrow down your results: Filtering allows users to limit the data shown in the dashboard to only those records that meet certain criteria, thus focusing on more relevant subsets of data.

D. Grouping by a dimension: Grouping involves categorizing data based on specific fields (dimensions), which helps in analyzing the data across different segments or categories.

When extending the Sales Operations app to distribute it as an Einstein Analytics template app, the correct endpoint to post the folderSource ID (as shown in the JSON snippet provided) is "/services/data/vXX.X/wave/templates". This endpoint is used for managing template apps in Einstein Analytics, allowing the consultant to package and distribute standardized analytical content across different Salesforce instances or to different user groups within the organization.