Certified-Tableau-CRM-and-Einstein-Discovery-Consultant

After passing the Community Cloud Adviser test in August 2020, I decided my coming thing was Tableau CRM and Einstein Discovery Adviser ( formerly Einstein Analytics and Discovery Adviser). Demand for this qualification is high. Analytics, Data Science, and Business Intelligence are an important and expanding part of Salesforce immolations.

It took nearly six weeks full- time to get ready for this test. In this companion I'll tell you about the trip, numerous surprises, and a different collection of useful study coffers from unanticipated places.

Who is the Ideal seeker?

The Salesforce Service Cloud Adviser credential is designed for those who have experience enforcing Service Cloud results in a client- facing part. campaigners should be suitable to successfully design and apply Service Cloud results that meet client business conditions, are justifiable and scalable, and contribute to long- term client success.

The ideal seeker for taking the Certified-Tableau-CRM-and-Einstein-Discovery-Consultant Adviser instrument is someone who has been laboriously working with the product and has hands- on experience with data ingestion processes, security and access executions, and dashboard creation.

By taking this credential, you ’ll demonstrate your faculty to design and make apps, datasets, dashboards in Tableau CRM( formerly Einstein Analytics) and stories in Einstein Discovery.

Crucial motifs

As always, this test is made up of motifs with varying weightings. It’s important to pay attention to the weightings so that you concentrate on preparing for motifs that will have the topmost number of questions on the test.

There are six test motifs in the Tableau CRM and Einstein Discovery Adviser test, and importantly, four of these test motifs represent a fifth of the test each. Below are the test motifs, presented in the sequence I would suggest you study for the test.

1. Data Layer 24

We know Salesforce as a database of tables, or objects, related to each other through record keys, parents, children, affiliated lists, or lookups. Datasets in Einstein are the polar contrary. Einstein datasets are single denormalized successional lists, optimized by Salesforce for read performance. The Data Layer in Einstein is each about rooting data from sources, also transubstantiating and loading it into Einstein as an Einstein Data Set.

·        prize, transfigure, and cargo data into Einstein Analytics

·        Name and serve of all data metamorphoses

·        produce datasets

·        utensil refreshes and data sync

·        Using the dataset builder

·        What's the difference between a form and data inflow

·        law data flows and fashions.

·        What's extended metadata( XMD)

·        How to use XMD

·        Combine data from multiple datasets or connected objects.

·        Write back

2. Analytics Dashboard Design 19

Dashboard design in Einstein means exactly that. How to design and apply a dashboard that meets a set of conditions.

·        How to use templated andpre-built apps

·        What are the features and advantages of using templated apps?

·        What's a dashboard contrivance?

·        Apply stylish practices of appearance and design

·        Template customization

·        Determine the stylish type of map for a given problem

·        Types of Einstein Discovery maps, similar as pie, line, channel, bar, piled bar

·        What's the Event Monitoring app and how is it set up?

3. Einstein Discovery Story Design 19

Einstein Discovery is the analytics tool for understanding and interpreting deals data, and making prognostications and opinions grounded on that data. tête-à-tête I set up this the most intriguing part of Einstein. It requires an elevated position of licensing known as Einstein Analytics Plus and there are test questions about this.

·        What's an Einstein story?

·        Prepare data for story affair

·        dissect story affair

·        Make applicable suggestions for enhancement grounded on a story

·        utensil Salesforce object write- reverse

·        Explain perceptivity Descriptive, individual, prophetic , conventional, picky

·        What's an outgrowth variable in Einstein?

·        How does an outgrowth variable relate to independent and dependent variables?

·        What types of perceptivity live in Einstein Discovery?

·        What areover-fitting and under- fitting?

·        What's the GINI measure?

·        What's the difference between Einstein Analytics Platform and Einstein Analytics Plus?

4. Analytics Dashboard perpetration 18

Dashboards in Einstein are special because they're interactive to a position you don't see in Salesforce. druggies can perform what- if analysis and see the results modelled incontinently.

·        How to use the Event Monitoring Analytics App

·        Code lenses and pollutants

·        How to use compare tables

·        Trend reports

·        What's faceting?

·        Time- series analysis reports

·        What's a timetable heat chart and how is it used?

·        What are tapes and relations?

·        Changing ornamental attributes of maps

·        Where are templates stored?

·        What's the command syntax to query a template depository?

5. Administration 9

Einstein is a different product; system administration, operation, and security work else. There are druggies, biographies, licenses, and authorization set licenses to understand.

·        Manage dataset extended metadata

·        Migration from sandbox to product

·        Settings and options to ameliorate dashboard and query performance

·        How to use the Dashboard Inspector

·        How to use change setsElements that can and can't be stationed in a change set

·        What are Integration stoner, security stoner, integration stoner profile, security stoner profile, integration stoner license?

·        What's the difference between authorization sets for Einstein Analytics Platform and Einstein Analytics Plus?

·        Currency operation

6. Security 11

Security, just like Administration, is different with Einstein. Field position security( FLS) doesn't live. There are still druggies, groups, and biographies, but there are new standard druggies, Einstein specific biographies, authorization sets, licenses, and Einstein authorization set licenses on Exam Labs Dumps. Security predicates are unique to Einstein. There's app- position sharing and new ways of doing row- position security.

·        How to control row, column, data set, and app security

·        What's app- position sharing?

·        How to use places to control access?

·        How does app- position sharing work?

·        What are app- places director, bystander, editor, and designed

·        What's participating heritage, how does it work, and how is it different in Einstein?

·        What's a security predicate?

·        How is a security predicate used?

·        How is a security predicate enciphered?