Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Exam Practice Test

The steps in the data exploration process in Salesforce Tableau CRM are as follows:

• View: Initially viewing the data to understand what is available and identify areas of interest.
• Explore: Digging deeper into the data to discover patterns, anomalies, and insights.
• Refine: Adjusting the exploration by filtering, sorting, or enhancing the data visualization for clearer insights.
• Save: Saving the state of your exploration to revisit or continue working on later.
• Share: Sharing your findings with others in the organization, either through direct dashboard access, downloadable reports, or URLs.

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

Predictive insights are primarily beneficial for:

• C: Drilling down into the underlying reasons behind a prediction. This capability enables users to understand not just what might happen in the future but why those outcomes are likely, based on the data being analyzed. This understanding is crucial for strategic decision-making and for optimizing processes based on predictive analytics.

In statistical terms, the standard deviation measures the amount of variation or dispersion in a set of values. A higher standard deviation indicates that the values are more spread out from the mean (average). Among the options provided, a standard deviation of 16 suggests the highest degree of spread away from the average compared to the other values.

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.

To allow users who belong to an account team to see all the opportunities associated with that account in Einstein Analytics, the recommended actions are:

• Apply sharing inheritance: This ensures that the security settings and visibility of the Account object are inherited by related records, like Opportunities, making it easier to manage access based on existing Salesforce CRM security models.
• In the dataflow, extract the AccountTeamMember object and augment it with the Opportunity object using 'AccountId' as the join field: This method allows the consultant to directly link team members to the accounts and their opportunities, ensuring that visibility in Einstein Analytics mirrors the CRM's team-based permissions.

These approaches leverage Salesforce's inherent security features and ensure data visibility is consistent with user roles and relationships defined in the CRM system.

To meet the security requirements of allowing only a subset of users to access the dataset and ensuring they only see data relevant to their region, the consultant can:

• Use a security predicate in the dataset so users can see only rows belonging to their respective regions (B): This involves configuring row-level security using security predicates based on the UserRegion field, which filters data dynamically based on the user's region.
• Save the dataset under a new application and give "viewer" access to a public group containing the list of users allowed to see the dataset (D): This method segregates access at the application level, allowing only designated users to view the dataset within that application.

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

In Salesforce Analytics Query Language (SAQL), a keyword refers to specific commands or functions such as 'group', 'foreach', 'filter', and more. These keywords are essential for structuring queries and must be used in lowercase to be recognized by the system. Keywords control operations on data streams within the queries, facilitating complex data manipulations and analyses.

The edgemart transformation, which grants a dataflow access to an existing, registered dataset, is specified in the Dataflow Editor. This tool within Salesforce Tableau CRM allows users to define and manipulate dataflow transformations, integrating various datasets into the analytical environment.

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 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

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.

The correct operation to use with the Analytics External Data API to upload monthly target data, which includes replacing existing entries with new data each month, is:

• C: Overwrite. This operation ensures that each upload replaces the existing dataset entirely, which is necessary for scenarios where the target data changes every month, and historical precision needs to be maintained.

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.

To optimize data loads by extracting Salesforce objects ahead of time, the correct construct to use is "Data Sync (Replication)." This feature in Salesforce Einstein Analytics allows for the periodic refresh of datasets from Salesforce objects without needing to rerun the entire dataflow. This optimization helps in managing large datasets more efficiently by keeping the data up-to-date with less processing time and system load.

To access Einstein Analytics (now known as Tableau CRM), having just an Einstein Analytics Platform license is not sufficient. Users also require specific permissions to use analytics functionalities. The critical permission is "Use Analytics," which should be granted via a permission set. Without this permission, users can't access or use Tableau CRM, even if they have the necessary license.

In a scenario where Recipe3 depends on Dataset1 created by Recipe1, while Recipe1 and Recipe2 have no dependencies, the optimal scheduling strategy is to make Recipe1 and Recipe2 time-based and Recipe3 event-based, triggered by the successful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

The Actions menu in a chart within Salesforce Tableau CRM (Einstein Analytics) typically includes options that enhance interactivity and integration with Salesforce features. One of the key functionalities offered by the Actions menu is the ability to post to Chatter directly from a chart. This allows users to share insights, collaborate on data visualizations, and engage with team members directly within the flow of analytics. Reference: Salesforce Help - Chatter in Dashboards

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

Posting to Chatter from a Salesforce Wave (Tableau CRM) dashboard is done by selecting "Post to Chatter" from the Actions drop-down menu at the top of the dashboard. This feature allows users to directly communicate findings, share insights, or discuss data visible on the dashboard with other team members through Salesforce Chatter, enhancing collaboration and information sharing. Reference: Salesforce Documentation - Post to Chatter from Dashboards

The scenario described where Yasmine cannot see any rows in the Seed Bank Orders dataset while Hadiqa can, likely indicates that the dataset's security predicate was set up to restrict data visibility based on user roles or attributes. Security predicates in Salesforce Tableau CRM are used to implement row-level security, ensuring that users only see data pertinent to their role or permissions within the organization. This security setup is crucial for maintaining data privacy and compliance. Reference: Salesforce Help - Row-Level Security

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.

In Tableau CRM (formerly Einstein Analytics), a dataset recipe allows users to combine data from different datasets, enriching the data through transformations such as joins, aggregations, and calculated fields. This functionality enables users to integrate varied data sources into a unified dataset that can be further analyzed and visualized within the platform. Recipes provide a user-friendly interface to manipulate and merge data without the need for complex coding, making it accessible to users with varying levels of technical expertise. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_prep_recipes.htm &type=5

The likely missed step in the deployment process of a Tableau CRM analytic app that includes a recipe and several datasets and dashboards, where the datasets aren't appearing post-deployment, is that the recipe needs to be run. Here's what should be done:

• Navigate to the Tableau CRM Analytics Studio.
• Locate the deployed recipe within the app.
• Manually initiate the recipe to run, or verify if it's scheduled to run automatically. If not, set up a schedule.
• Ensure that once the recipe runs, it processes and creates the datasets as designed.

This step is crucial because recipes are responsible for transforming data and generating the datasets needed for the dashboards.

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.

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.

When Salesforce Analytics runs a dataflow that accesses Salesforce objects, it uses the Integration User. This is a system user configured to perform back-end operations that require interacting with Salesforce data, independent of any specific human user. This setup ensures that data operations such as dataflows are consistent and not tied to the permissions or roles of individual users, which could vary and affect the execution of such tasks. Reference: Salesforce Help - Dataflows and Integration User

To potentially improve an AUC score of .62 in a Tableau CRM story, consider the following strategies:

• A. Segment the training set: By segmenting the training set, you can ensure that the model trains on more homogeneously grouped data, which might help in improving the model’s predictive accuracy and, consequently, the AUC score.
• D. Add more rows of relevant data: Increasing the volume of relevant data can provide the model with more examples to learn from, which might enhance its ability to generalize and improve the AUC score.

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.

Two benefits of designing using the "Progressive Disclosure" principle are:

• A: Improved ease of use for end users. By only displaying essential information initially and allowing users to access more detailed data as needed, dashboards become less cluttered and more user-friendly.
• D: Better dashboard performance. This design principle can also lead to better performance as less data is processed and rendered initially, speeding up load times and responsiveness.

The correct timeseries statement to fix the SAQL query for predicting sales in each sales region is:

• C: q = timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Year', 'Date_Quarter', "Y-Q"), partition='Region', seasonality=4). This statement correctly specifies the required parameters for a timeseries analysis, including the date columns, the partition by 'Region', and the seasonality factor, which is crucial for accurate forecasting in time series analysis.

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.

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

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.

The Einstein Analytics Dashboard Inspector is a tool used to analyze and improve dashboard performance. It allows users to: B. Get a list of recommendations on how to improve the performance of the dashboard. This includes suggestions on query optimizations and step adjustments. C. View all queries and the time it took to run each one. This feature helps in identifying slow-running queries that may need optimization. D. View the total time required to run all queries. This gives an overall view of the dashboard's performance, which is crucial for troubleshooting and performance enhancement. These tools help in maintaining optimal dashboard performance and user experience. Reference: Salesforce Documentation on Dashboard Inspector

For accessing Tableau CRM data from remote applications or websites, the following methods are recommended:

• B Lightning Out enables Salesforce components, including Tableau CRM dashboards, to be embedded into external web applications, maintaining their interactive capabilities and security.

To automate the loading of data from a Microsoft Azure SQL Data Warehouse into an Einstein Analytics dataset, transform it, and merge it with company data daily, the following Einstein Analytics user interface features can be used:

• A: Dataflow: This tool allows for the creation and management of complex data transformation and integration processes necessary for combining data sources.
• D: Recipes: Recipes provide a user-friendly interface for performing data transformation tasks without writing code, which can be used for merging and transforming data.
• E: Analytics REST API: The API can automate data ingestion and management tasks, providing flexibility and control over data integration from external systems.

In the configuration wizard of Tableau CRM apps, required fields or questions without default values are typically indicated by the system to guide the setup process. By proceeding with the configuration ("Looks good, next"), the wizard will highlight any mandatory questions that have not been answered, usually by marking them in red. This visual cue allows users to easily identify and address the required inputs that are missing defaults, ensuring that essential configurations are not overlooked during setup. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_apps_configure.htm &type=5

The most straightforward and effective way to ensure that temperature data is easy to interpret on Einstein Analytics dashboards is to add a text widget to the dashboard indicating whether the temperature is in Fahrenheit or Celsius. This method provides clear, unambiguous context directly within the dashboard view, improving user understanding at a glance.

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 build a lens showing the total number of Opportunities and Cases per Account using two separate datasets, the consultant can:

• D: Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results. This method involves writing a SAQL query that groups the data by Account and aggregates the number of Opportunities and Cases, providing a unified view of the data across both datasets.

For Universal Containers to automate the creation of datasets from their on-premise data warehouse, the best solution would be to utilize middleware with the Analytics External Data API. This approach enables the integration of on-premise data systems with Salesforce through a secure and scalable middleware, which can automate the data extraction and loading process into Tableau CRM, thus replacing the weekly manual process.