How to Pass Tableau CRM & Einstein Discovery Consultant Exam

How to Pass Tableau CRM & Einstein Discovery Consultant Exam

Last Updated on November 20, 2022 by Rakesh Gupta

It has been approx three years since I pass the Einstein Analytics and Discovery Consultant exam. In the past few weeks, many people reached out to me asking for guidance and a path to becoming a certified Einstein Analytics and Discovery Consultant.

That gives me an idea for writing a blog post on this topic. For by reading from the beginning to the end of this article, you will have a clear understanding of – and will be able to devise a plan and a strategy for – how to pass the Einstein Analytics and Discovery Consultant Certification exam.

👉 As you are here, you may want to check out the following articles:

  1. How to Pass Salesforce Pardot Consultant Certification Exam
  2. How to Pass Salesforce Field Service Consultant Certification Exam

So, Who is an Ideal Candidate for the Exam?

The Salesforce Certified Tableau CRM and Einstein Discovery Consultant credential is intended for individuals who have the knowledge, skills, and experience with data ingestion processes, security and access implementations, and dashboard creation. This credential encompasses the fundamental knowledge and skills to design, build, and support apps, datasets, dashboards and stories in Tableau CRM and Einstein Discovery.

The Salesforce Certified Tableau CRM and Einstein Discovery Consultant generally has a minimum of one year of experience and skills across the Tableau CRM and Einstein Discovery domains, including:

  • Front End
  • Back End
  • Administrative/Middle
  • Einstein Discovery

How to prepare for the exam?

Learning styles differ widely – so there is no magic formula that one can follow to clear an exam. The best practice is to study for a few hours daily – rain or shine! Below are some details about the exam and study materials:

  • 60 multiple-choice/multiple-select questions – 90 mins
  • 68% is the passing score
  • Exam Sections and Weighting
    • Data Layer: 24%
    • Security: 11%
    • Administration: 9%
    • Tableau CRM Dashboard Design: 19%
    • Tableau CRM Dashboard Implementation: 18%
    • Einstein Discovery Story Design: 19%
  • The exam Fee is $200 plus applicable taxes
  • Retake fee: $100
  • Schedule your certification exam here

The following list is not exhaustive; so check it out and use it as a starting point:

  1. Salesforce Certified Tableau CRM and Einstein Discovery Consultant Exam Guide
  2. Trailmix: Learn Tableau CRM Plus
  3. Tableau CRM Training by Consultant Academy: 10+ Hours Videos
    1. Consultant Academy Link
    2. YouTube Link
  4. Trailhead Trails:
    1. Explore with Tableau CRM
    2. Gain Insight with Einstein Discovery
    3. Tableau CRM Apps Basics
    4. Build and Administer Tableau CRM
    5. Accelerate Tableau CRM with Apps
  5. EA Tech Lounge Video Series: 20+ Hours Videos
  6. Trailhead Superbadges:
    1. CRM Analytics Data Preparation Specialist
    2. CRM Analytics and Einstein Discovery Insights Specialist
  7. Instructor Led training by Trailhead Academy
    1. Building Lenses, Dashboards and Apps in Einstein Analytics (ANC201)
    2. Implement and Manage Tableau CRM (ANC301)

What you Need to Know to Smoothen your Journey

On a very high level, you have to understand the following topics to clear the exam. There is no shortcut to success. Read and practice as much as you can. All credit goes to the Salesforce Trailhead team and their respective owners.

  1. Let’s start from basic – Even though you haven’t work on any analytics and business intelligence (BI) tool (or even is IT world) and want to become certified Einstein Analytics and Discovery Consultant, the following steps will help you to achieve your goal.
    1. Learn SQL
      1. freeCodeCamp is an open-source community that helps people learn to code. If you’ve never studied databases or SQL before, this is a great starting point. The course starts off with Mike helping you install MySQL on Windows or Mac. Then he explores topics like schema design, Create-Read-Update-Delete operations (CRUD), and other database fundamentals.

        1. The SQL knowledge will help you when you start working with SAQL or write custom queries in CRM Analytics.  
      2. Finish SQL and Databases – A Full Course for Beginners course.
      3. You can companion your learning with a SQL for Data Science course. In this UC Davis course, you will learn the basics of how to use SQL in the context of Data Science. This course is free to audit on Coursera.
    2. Understand JSON
      1. JSON, or JavaScript Object Notation, is a format used to represent data. It was introduced in the early 2000s as part of JavaScript and gradually expanded to become the most common medium for describing and exchanging text-based data. Today, JSON is the universal standard of data exchange.
      2. While working on CRM Analytics several times requires updating the JSON to create the desired dashboard. One of the best examples: Is creating a Dynamic Gauge Chart.
      3. The purpose of this exercise is you understand what is JSON, Syntax and how to edit it.
        1. JSON for Beginners – JavaScript Object Notation Explained in Plain English
        2. Learn JSON – Full Crash Course for Beginners
    3. Now it is time to get started with CRM Analytics, formerly known as Tableau CRM.
      1. Complete Explore with Tableau CRM Trailhead module
      2. Complete this training: Tableau CRM Training by Consultant Academy: 10+ Hours Videos
        1. Consultant Academy Link
        2. YouTube Link
        3. The key for success is to practice as much as possible in your developer org. Get a Free Tableau CRM-enabled Developer Edition. 
  2. Data Layer: 24%
    1. A dataset is set of specially formatted source data, optimized for interactive exploration.
    2. Use a Data Prep (Recipes) recipe in CRM Analytics to clean, transform, and enrich data before loading data into one or more targets.
    3. A lens is a saved exploration. You’ll go more in depth about lenses later in the trail when you do your first explorations.
    4. A dashboard is a curated set of charts, metrics, and tables that gives you an interactive view of your business data.
    5. An app is a purpose-built set of analyses and answers about a specific area of your business. With apps, you can provide curated paths through your data, plus powerful tools for spontaneous, deep explorations. After creating dashboards, lenses, and datasets, you can organize them in apps to present assets in relevant order, and then share apps with appropriate people and groups.  
    6. The CRM Analytics dashboard component is an Aura component used to embed CRM Analytics dashboards in Visualforce and Lightning pages. The component can render a live CRM Analytics dashboard or it can be interactive with the page using events and methods to update the dashboard state.
    7. A story defines the data and analytical settings that Einstein Discovery uses to generate insights and build predictive models. Story settings include the outcome variable, whether to maximize or minimize the outcome variable, the data to analyze in a CRM Analytics dataset, and other preferences.
    8. CRM Analytics connectors give you an easy way to connect data inside and outside of Salesforce with CRM Analytics.CRM Analytics provides a prebuilt connector for data in your local org and a range of configurable connectors for remote data in external Salesforce orgs, apps, data warehouses, and database services.
    9. Before you run or schedule data sync, specify whether the sync extracts incremental changes or all records from each Salesforce object. By default, CRM Analytics performs an incremental sync. An incremental sync runs faster because it extracts only the latest changes to the Salesforce object.
      1. During the first sync of an object, CRM Analytics always performs a full sync. Switching the site or migrating the org also triggers the object to undergo a full sync.
      2. Incremental sync isn’t supported for Salesforce big objects or these objects.
        1. CallCenter
        2. CaseTeamMember
        3. CategoryNode
        4. CollaborationGroup
        5. CollaborationGroupMember
        6. CollaborationGroupMemberRequest
        7. Division
        8. Domain
        9. DomainSite
        10. Group
        11. GroupMember
        12. ModelFactor
        13. Profile
        14. Site
        15. Territory
        16. Topic
        17. User
        18. UserRole
        19. UserTerritory
      3. Run data sync manually the first time to make the data available in CRM Analytics to build recipes. Schedule subsequent syncs to regularly update the data.
      4. Run full sync for objects containing formula fields. With incremental sync, formula fields can become out of sync with your synced object.
      5. To ensure that all updates are included, set the object’s connection mode to Periodic Full Sync.
      6. To ensure that the latest source data is loaded into datasets, schedule data syncs to pull data into CRM Analytics before the corresponding recipes. You schedule data sync for each connection, where all objects under the connection sync at the specified time, and not individual objects. To sync objects from the same data source on different intervals, create multiple connections to the data source, and set a unique schedule for each connection.
    10. Monitor the progress of CRM Analytics data syncs in Data Manager.
    11. Set Data Sync notifications to receive an email notification when a CRM Analytics sync job has warnings, when sync fails, or every time the sync finishes.
    12. Best Practices to Avoid Canceled Jobs Due to Overlapping Schedules
      1. Schedule your recipe and data sync jobs with plenty of time between the runs to allow for potential delays.
      2. Periodically review your job runs to see how long an average job takes, and update the schedule to allow for potential delays.
      3. Split large data sync jobs into multiple smaller data sync jobs using additional remote or local connections to the same data source. You can set more frequent sync schedules for smaller groups of objects that require more frequent updates, and infrequent sync schedules for less-updated objects.
      4. Enable priority scheduling to automatically queue shorter or smaller runs before longer or larger runs.
    13. Keep these behaviors in mind when creating or updating a dataflow with Data Sync enabled.
      1. When you upload a dataflow definition file or update a dataflow in the dataflow editor, CRM Analytics validates the definition file or dataflow. If you see errors displayed, correct them, and upload the file or update the dataflow again.
      2. Dataflow definition file uploads can take longer because CRM Analytics is validating the file and using the sfdcDigest nodes to define sync settings.
      3. When you remove an object’s sfdcDigest node from the definition file, sync is not disabled for that object. If necessary, disconnect the object from sync on the Connect tab of the data manager.
      4. When you remove a field from an sfdcDigest node, the field is still included for sync. If necessary, exclude the field in the objects sync settings.
      5. When you add a field in an sfdcDigest node, the field is included for data sync and a sync is triggered for the object when the dataflow next runs.
      6. When you unschedule the dataflow, CRM Analytics does not disconnect the Salesforce objects and fields from sync.
    14. Edit a dataset to change its name, app, security, or extended metadata (XMD). You can also replace data in a dataset, restore it to a previous version, or delete it. The dataset edit page also provides key information about when the dataset was created and last updated, and where it is used.
      1. Restoring a dataset has no effect on associated dataflows or recipes. It’s possible that when an associated dataflow or recipe next runs, it can undo the results of a restore.
      2. Before you delete a dataset, consider the following guidelines.
        1. You can’t recover a deleted dataset.
        2. Use the data manager to delete datasets from another user’s My Private App. You can’t see or delete other users’ private datasets from the CRM Analytics home or app tabs. For security reasons, you also can’t view the data in other users’ private datasets.
        3. You also can’t delete a dataset that’s used in a dashboard, lens, or dataflow. Before you delete a dataset, first remove references to it from dashboards or dataflow transformations, and delete associated lenses.
        4. Analytics doesn’t check or show if a dataset is used in recipes. Be sure to remove dataset references from recipes as well. If you delete a dataset that’s used in a recipe, the recipe fails the next time it runs.
    15. The External Data API enables you to upload external data files to CRM Analytics. The External Data API can upload .csv files, and you can optionally specify the structure of your data by defining metadata in JSON format.
      1. The high-level steps for uploading external data by using the API are:
        1. Prepare your data in CSV format, and then create a metadata file to specify the structure of the data.
        2. Connect programmatically to your Salesforce organization.
        3. Configure the upload by inserting a row into the InsightsExternalData object, and then set input values such as the name of the dataset, the format of the data, and the operation to perform on the data.
        4. Split your data into 10-MB chunks, and then upload the chunks to InsightsExternalDataPart objects.
        5. Start the upload by updating the Action field in the InsightsExternalData object.
        6. Monitor the InsightsExternalData object for status updates, and then verify that the file upload was successful.
    16. To set up access to source data, create a connection. When you create a connection, select objects and columns to pull data from. You can add a filter to the connection to extract a subset of all rows. In the connection properties, you also specify a user account that determines what data the connection can access. For example, to access data in Amazon S3, specify an Amazon S3 user account. If the user account doesn’t have access to an object, the connection can’t pull data from that object.
      1. After you create a connection, run its data sync to extract the data from each selected object in the data source and store it in the corresponding CRM Analytics connected object. After you run a data sync for the first time, you can add the connected objects as sources for recipes. In data prep, you can add transformations to prepare the data in the connected objects and output the results into datasets.
      2. Run the recipe to create datasets. Continue to run them to refresh the data. You can run data sync and recipes on demand. You can also schedule them to run on an ongoing basis. To ensure that your recipes use the latest data, schedule data sync jobs to complete before dependent recipes run.
      3. Considerations Before Integrating Data into Datasets
        1. Handle Numeric Values – CRM Analytics internally stores numeric values in datasets as long values. For example, CRM Analytics stores the number 3,200.99 with a scale of 2 as 320099. The user interface converts the stored value back to decimal notation to display the number as 3200.99.
        2. Handle Date Values – When CRM Analytics loads dates into a dataset, it breaks up each date into multiple columns, such as day, week, month, quarter, and year, based on the calendar year. For example, if you extract dates from a CreateDate column, CRM Analytics generates columns such as CreateDate_Day and CreateDate_Week. If your fiscal year differs from the calendar year, you can enable CRM Analytics to generate fiscal date columns as well.
        3. Handle Custom Time Zone Values – Time zone support lets you view time-specific data on dashboards in a time zone that you specify for your org. By default, CRM Analytics datasets aren’t time-zone aware, so CRM Analytics treats all date-time values as being in GMT. The data you see on your dashboards is in GMT, regardless of your local time zone. When you enable time zone support, CRM Analytics converts date-time values in your datasets to the time zone selected for CRM Analytics. You can then create time zone enabled dashboards to display these converted date-time values. Users see dashboard data in the single custom time zone you set, not their personal timezone specified in Salesforce.
        4. Handle Text Values – Confirm that text values in a column are uniform in formatting, spelling, and language. Inconsistencies can occur within data sources and after merging data from multiple data sources.
        5. Dataset Capacity and Limits – Before you create any datasets, review the limits. For example, each Salesforce org has a maximum number of rows for all datasets in the org. There are also limits on the number of columns in a dataset and characters in a column.
        6. Reserved Dataset Field NamesCRM Analytics data prep doesn’t support using some reserved keywords as field names in explorer lenses and dashboards.
          1. all
          2. ALL
          3. count
    17. Clean, Transform, and Load Data with Data Prep
    18. Run Data Sync and Recipes to Create and Refresh Datasets
    19. Get Started Faster with Data Templates
    20. Each dataset supports up to 2 billion rows. If your Salesforce org has less than 2 billion allocated rows, then each dataset supports up to your org’s allocated rows.
    21. Dataset Field Limits
      Limit Value
      Maximum number of fields in a dataset 5,000 (including up to 1,000 date fields)
      Maximum number of decimal places for each value in a numeric field in a dataset (overflow limit) 17 decimal places

      When a value exceeds the maximum number of decimal places, it overflows. Both 100,000,000,000,000,000 and 10,000,000,000,000,000.0 overflow because they use more than 17 decimal places. A number also overflows if it’s greater (or less) than the maximum (or minimum) supported value. 36,028,797,018,963,968 overflows because its value is greater than 36,028,797,018,963,967. -36,028,797,018,963,968 overflows because it’s less than -36,028,797,018,963,967.

      When a number overflows, the resulting behavior in CRM Analytics is unpredictable. Sometimes CRM Analytics throws an error. Sometimes it replaces a numeric value with a null value. And sometimes mathematical calculations, such as sums or averages, return incorrect results. Occasionally, CRM Analytics handles numbers up to 19 digits without overflowing because they are within the maximum value for a 64-bit signed integer (263 – 1). But numbers of these lengths aren’t guaranteed to process.

      As a best practice, stick with numbers that are 17 decimal places or fewer. If numbers that would overflow are necessary, setting lower precision and scale on the dataset containing the large numbers sometimes prevents overflow.

      Maximum value for each numeric field in a dataset, including decimal places 36,028,797,018,963,967

      For example, if three decimal places are used, the maximum value is 36,028,797,018,963.967

      Minimum value for each numeric field in a dataset, including decimal places -36,028,797,018,963,968

      For example, if five decimal places are used, the minimum value is -36,028,797,018,9.63968

      Maximum number of characters in a field 32,000
  3. Security: 11%
    1. App-Level Sharing
      1. CRM Analytics apps are like folders, allowing users to organize their own data projects—both private and shared—and control sharing of dataset, lenses, and dashboard.
      2. Each user also has access to a default app out of the box, called My Private App, intended for personal projects in progress. The contents of each user’s My Private App aren’t visible to administrators, but dashboards and lenses in My Private App can be shared.
      3. All other apps created by individual users are private, by default; the app owner and administrators have Manager access and can extend access to other users, groups, or roles.
      4. To enable others to see a lens, dashboard, or dataset, one way to share is by sharing the app it’s in.
      5. Summary of what users can do with Viewer, Editor, and Manager access.
        Action Viewer Editor Manager
        View dashboard, lenses, and dataset in the app X X X
        See who has access to the app X X X
        Explore datasets that the user has Viewer access to and save lenses to an app that the user has Editor or Manager access to X X X
        Save contents of the app to another app that the user has Editor or Manager access to X X X
        Save changes to existing dashboard, lenses, and dataset in the app (saving dashboard requires the appropriate permission set license and permission) X X
        Change the app’s sharing settings X
        Rename the app X
        Update asset visibility in an app X X
        Delete the app X
      6. If you have Manager access to an app, you can delete it. Deleting an app permanently removes all of its lenses, dashboards, and datasets from CRM Analytics.
    2. Dataset Security to Control Access to Rows
      1. If a CRM Analytics user has access to a dataset, the user has access to all records in the dataset by default. To restrict access to records, you can implement row-level security on a dataset when you use sharing inheritance and security predicates. Sharing inheritance automatically applies a Salesforce object’s sharing logic to the dataset’s rows. A security predicate is a manually assigned filter condition that defines dataset row access.
        1. CRM Analytics: Data Security to Control Access to Rows
        2. Add Row-Level Security with a Security Predicate
        3. Add Row-Level Security by Inheriting Sharing Rules
      2. Sharing inheritance can be applied from a supported object if all object records have fewer than 400 sharing descriptors each. Supported objects for sharing inheritance are:
        1. Account
        2. Case
        3. Contact
        4. Lead
        5. Opportunity
      3. It’s best practice to have a defined security predicate for datasets using inherited sharing. Without a security predicate, users not covered by sharing inheritance see no data in the dataset because they have no dataset row-level access.
      4. Sharing isn’t automatically applied to datasets. You apply sharing to each dataset manually.
      5. Sharing inheritance can affect the performance of queries, dataflows, and Data Prep recipes. If your requirements include best-possible performance, use security predicates instead of sharing inheritance.
      6. Changes to the rowLevelSharingSource or rowLevelSecurityFilter security settings in a dataflow only affect datasets created after you save the change. Similarly, changes to a Data Prep recipe output node’s Sharing Source and Security Predicate fields only affect datasets created after you save the change. Update those settings for existing datasets on the edit dataset page.
      7. A dataset can inherit sharing settings from only one object, regardless of how many source objects are used to create the dataset. Because many objects comprise the dataset, each object can use a different security model.
      8. Calculated fields are treated as normal fields. Row-level security applied during the calculation in Salesforce is ignored.
    3. Predicate Expression Syntax for Datasets
      1. You must use valid syntax when defining the predicate expression.
        1. <dataset column> <operator> <value>
      2. Consider the following requirements for the predicate expression:
        1. The expression is case-sensitive.
        2. The expression cannot exceed 5,000 characters.
        3. There must be at least one space between the dataset column and the operator, between the operator and the value, and before and after logical operators. This expression is not valid: ‘Revenue’>100. It must have spaces like this: ‘Revenue’ > 100.
  4. Administration: 9%
    1. Each CRM Analytics Growth and CRM Analytics Plus license is a single-user license that provides access to CRM Analytics. The license limits your instance of the CRM Analytics to 1 billion rows of data. If you require more data, you can purchase CRM Analytics – Additional Data Rows, which entitles you to 100 million more rows.
    2. The CRM Analytics Growth license includes two prebuilt permission sets:
      1. CRM Analytics Growth Admin enables all permissions required to administer the CRM Analytics platform, including permissions to create and manage CRM Analytics templated apps and Apps.
      2. CRM Analytics Growth User enables all permissions required to use the CRM Analytics platform and CRM Analytics templated apps and Apps.
    3. The CRM Analytics Plus license includes two prebuilt permission sets:
      1. CRM Analytics Plus Admin enables all permissions required to administer the CRM Analytics platform and Einstein Discovery, including permissions to create and manage CRM Analytics templated apps and Apps.
      2. CRM Analytics Plus User enables all permissions required to use the CRM Analytics platform, Einstein Discovery, and CRM Analytics templated apps and Apps.
    4. You can assign a CRM Analytics permission set license along with any of the following Salesforce user licenses:
      1. Lightning Platform (app subscription)
      2. Lightning Platform (one app)
      3. Full CRM
      4. Salesforce Platform
      5. Salesforce Platform One
    5. Deploy CRM Analytics Prebuilt Apps
    6. CRM Analytics Encryption
    7. CRM Analytics Limitations
  5. Tableau CRM Dashboard Design: 19%
    1. Follow best practices to design and build useful, effective CRM Analytics dashboards, while minimizing rework and addressing potential gaps.
    2. Before you build the dashboard, take into account the following design best practices:
      1. Sketch your dashboard on paper or a whiteboard before you start building.
      2. Prioritize elements, top left to bottom right. With languages that are read left to right, people start by looking at the top left corner and working their way down. Consider the audience’s language and design for it. If your audience has limited time or attention, place important elements where they will be noticed.
      3. Place high-level, easy-to-read, actionable widgets near the top left, and place widgets with supporting information lower. For example, place numbers that display a single measure, such as revenue for the current quarter, high and to the left.
      4. Group filters together at the top or left so that they are quickly noticeable. You can use a container widget to section them off in the dashboard.
      5. Keep in mind that a chart in CRM Analytics is primarily a way to ask questions, not a way to illustrate a conclusion. A good dashboard invites the audience to drill down and seek ever more focused and useful information.
      6. Choose chart types based on the characteristics of the data, not for look or variety. For example, if most of your charts display value changes over time, it’s OK if they’re all line graphs.
      7. If a chart seems to need a lengthy caption or title, reconsider whether the chart is doing its job. Well-chosen data often speaks for itself.
      8. Use container widgets to frame and organize related elements in the dashboard.
      9. While you build the dashboard:
        1. Apply labels to sections and charts to annotate the dashboard.Each chart in the dashboard contains a label.
        2. Use colors to define sections.The dashboard uses colors to separate key performance metrics and filters.
        3. Don’t clutter the dashboard—leave some empty space. If needed, break a dashboard into a series of dashboards, using link widgets to help the user navigate them.
      10. After you build the dashboard
        1. Have users review the dashboard before making it final. You can post a dashboard to Chatter to get feedback. Users can annotate the widgets in the dashboard to have conversations about the content.
  6. Tableau CRM Dashboard Implementation: 18%
    1. See what unexpected insights you can surface by interactively exploring and visualizing your data, using explorer tools.
    2. Use tables to get a view of the data that is close to the underlying dataset, and you can use tables to manipulate and extend the data to expose fresh insights. With values, compare, and pivot tables, CRM Analytics explorer gives you options for both of those goals.
    3. If your goal is to understand vast amounts of business data, and to communicate that understanding with coworkers, partners, and customers, being able to visualize your data is critical. CRM Analytics provides a chart for every need, each a means for illustrating key aspects of your business in just the right way.
    4. Build a CRM Analytics dashboard to continuously monitor key metrics of your business, analyzing the results by key dimensions, like region, products, and time period. Add interactive charts that synthesize information into an easy-to-read format. To complement the charts, add tables that show record-level details. Add filters to allow dashboard viewers to change the focus of the results. Create customized layouts to optimize the display of a dashboard on different types of devices, like mobile phones, tablets, and desktops.
    5. Dashboard templates speed your analytics development by automatically creating dashboards. Some provide blank layouts that you populate with data, while other “smart” templates create dashboards that require little to no additional configuration.
    6. Dashboard components are a type of dashboard widget that can contain other widgets, pages, and Lightning Web Components. Use dashboard components to manage and reuse groups of charts, tables, filters, text, and more in multiple dashboards. Use Lightning Web Components to bring custom Lightning Experience functionality directly into dashboards.
    7. Use repeater widgets to show select fields from a query in a scrollable list in your dashboard. Create a customized layout of text, numbers, charts, and images in a repeater widget, and your dashboard users can scroll through a stylized view of query data.
    8. Make the information on a dashboard easier to digest by chunking the content into multiple pages. And with fewer queries per page, dashboard performance increases. With pages, you can tell a story by creating a dynamic pathway through your dashboard.
    9. Widgets are the basic building blocks of a dashboard. In the dashboard designer, you can add different widgets to perform functions. For example, widgets can calculate key performance indicators, filter dashboard results, visualize your data using interactive charts, and show record-level details in tables.
    10. Queries return results that are displayed in widgets. For example, a number widget displays the result of a calculation that is defined in a query. Queries can be built on a data source, like a dataset or a Salesforce object. They can also be “custom queries” created with user-defined values.
    11. Set the initial selections and global filters that appear when the dashboard first opens. To analyze the results from a different angle, the dashboard viewer can change the initial selections and, if configured, global filters while viewing the dashboard.
    12. Before you finalize the dashboard, run a performance check on the dashboard and its queries to ensure that everything is running optimally. The dashboard inspector identifies different types of bottlenecks, like query issues and redundant queries, and provides recommendations to improve performance. Because dashboard layouts can contain different widgets (and queries), run the inspector on each layout. If a dashboard contains multiple pages, run the inspector on each page. The inspector provides results only for the current page.
    13. Extend CRM Analytics everywhere throughout your business. The CRM Analytics visualizations you’ve built are more powerful when you share them across your Salesforce experience by integrating them into custom pages, Visualforce pages, Experience Cloud sites, and more. In addition, custom menus in lenses and dashboards allow you to perform common Salesforce actions directly from CRM Analytics.
      1. Compare the options for embedding dashboards. To learn more about embedding dashboards for mobile users.
    14. Optimize Dashboard Performance
  7. Einstein Discovery Story Design: 19%
    1. Einstein Discovery is AI-Powered analytics that enables business users to automatically discover relevant patterns based on their data – without having to build sophisticated data models.
      1. Automated Analytics – Analyze millions of data combinations in minutes.
      2. Unbiased Insights – Understand what happened, why it happened, what could happen, and what to do about it.
      3. Narrative Explanations – Natural language-based insights and stories exported to Salesforce or Microsoft Office.
      4. Recommended Actions – Take action, stay on top of changes, and iterate.
    2. You can explore insights for any story to which you have access. An insight is a statistically significant finding in your data. When you create a story version, Einstein Discovery analyzes the data in your dataset and generates insights based on its analysis. Insights provide a starting point for you to investigate the relationships among your story’s explanatory variables and its goal.
    3. Model metrics reveal quality measures and associated details for a model. Use model metrics to evaluate a model’s ability to predict an outcome. When ready, you then deploy a model to Salesforce to predict outcomes in production.
      1. The metrics that are visible in the Model Metrics tabs depend on the use case (binary classification, numeric, or multiclass classification) for the outcome variable in your story.
        1. The numeric use case is based on outcomes that are numeric variables. The Model Metrics tabs show quality statistics associated with linear regression models.
        2. The binary classification use case is based on text (categorical) variables with binary outcomes. The Model Metrics tabs show quality statistics associated with logistic regression models.
        3. The multiclass classification use case is based on categorical variables with 3-10 possible outcomes. The Model Metrics tabs show quality statistics associated with multiclass models.
    4. Einstein Discovery Capacities and Limits

Additional Resources

A few blogs/videos help you prepare for the Einstein Analytics and Discovery Consultant exam.

  1. CRM Analytics Design Guide
  2. Let’s Play Salesforce – By Peter Lyons
  3. Salesforce Blogger – By Rikke Hovgaard
  4. CRM Analytics Security Implementation Guide
  5. Analytics SAQL Developer Guide
  6. SQL for CRM Analytics
  7. CRM Analytics Dashboard JSON Overview
  8. CRM Analytics Glossary
  9. Learning Resources

Conclusion

If you have basic experience with all the above topics, passing the exam will be a cinch, and you will be able to earn the much-coveted Salesforce Certified Einstein Analytics and Discovery certification exam! However, if you do not have enough experience (4-6 months) with the CRM Analytics and plan to become a Certified Einstein Analytics and Discovery Consultant. I suggest you draw a 8-12 weeks plan (finish the above Trailhead to prepare for it).

I hope that you find these tips and resources useful. If you put the time and effort in, you will succeed. Happy studying and good luck!

Formative Assessment:

I want to hear from you!

Have you taken the Salesforce Certified Einstein Analytics and Discovery consultant exam? Are you preparing for the exam now? Share your tips in the comments!

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