DevLife.Tech Einstein Analytics Exam Guide
This exam guide provides information about the Salesforce Certified Einstein Analytics and Discovery Consultant exam and relevant important documentation.

Key Highlights

After reading this article, you’ll be able to:

  • Understand the target audience of the exam
  • Learn the characteristics of the exam
  • Understand exam outlines
  • Know topic wise important training references & documentation
  • Quick cheat sheet for last-minute references

Getting Started

Target Audience

The Salesforce Certified Einstein Analytics and Discovery Consultant exam is intended for an individual who has a broad knowledge of the Einstein Analytics and Discovery platform and its capabilities, skills, and experience with data ingestion processes, security, and access implementations, and dashboard creation. Good to but not mandatory to have a minimum of one year of experience and skills across the Einstein Analytics and Einstein Discovery domains.

Exam Characteristics

Details about Salesforce Einstein Analytics and Discovery Consultant exam.

  • Content: 60 multiple-choice questions and 5 non scored questions
  • Time allotted to complete the exam: 90 minutes
  • Passing score: 68%
  • Registration fee: $200 + applicable taxes
  • Retake fee: $100 + applicable taxes
  • Delivery options: Online or Onsite Proctored
  • Prerequisite: None

Exam Outline

Exam time can be a very stressful time and with so much riding on its result, it requires a lot of hard work and dedication. Planning ahead can sort out your goals and give you ample of time to try out things on your own. Here are objectives in order of higher to lower question weight-age which cover complete portion of certification exam.

Very important resource to follow for all Einstein analytics related documentation is the official Einstein learning map here.

DATA LAYER

  • Given data sources, use data managers to extract and load the data into the EA application to create datasets.
  • Given business needs and consolidated data, implement refreshes, replication, and recipes to appropriately solve the basic business needs.
  • Given a situation, demonstrate knowledge of what can be accomplished using the EA APIs.
  • Given a scenario, use EA to design a solution that accommodates data-flow limits.
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DevLife.Tech Data Layer

Sub-topic wise helpful documentation

  1. Data Manager / Datasets
  2. Refresh, Replication (Sync), Recipe
  3. Einstein Analytics API & Limits

SECURITY

  • Given governance and EA asset security requirements, implement necessary security settings including users, groups, and profiles.
  • Given row-based security requirements and security predicates, implement the appropriate datasets security settings.
  • Implement App sharing based on user, role, and group requirements.
DevLife.Tech Security

Sub-topic wise helpful documentation

  1. Security Settings
  2. Row Level / Predicate Security
  3. App Sharing

ADMIN

  • Using change management strategies, manage the migration from sandboxes to production org.
  • Given a scenario, improve dashboard performance by restructuring the datasets and data using lenses, pages, and filters.
  • Given a user requirement, manage datasets extended metadata by affecting labels, values, and colors.
  • Given business and access requirements, enable QA, options, and access as expected.

Sub-topic wise helpful documentation

  1. Migration
  2. Extended Metadata
  3. Performance

ANALYTICS DASHBOARD DESIGN

  • Given a customer situation, determine and define their dashboarding needs.
  • Given customer requirements, create meaningful and relevant dashboards through the application of UX design principles and EA best practices.
  • Given business requirements, customize existing EA template apps to meet the needs.
DevLife.Tech Arch

Sub-topic wise helpful documentation

  1. Dashboard Needs
  2. Design / Best Practices
  3. Templates

ANALYTICS DASHBOARD IMPLEMENTATION

  • Given business requirements, define lens visualizations such as charts to use and dimensions and measures to display.
  • Given customer business requirements, develop selections, and result bindings with static steps.
  • Given business requirement that are beyond the standard Un use SAQL language to build lends, configure joins, and connect data sources.

Sub-topic wise helpful documentation

  1. Visualizations
  2. Bindings
  3. Time Series
  4. Compare Tables
  5. SAQL

EINSTEIN DISCOVERY STORY DESIGN

  • Given a dataset, use ED (Einstein Discovery) to prepare data for the story output by accessing data and adjusting outputs.
  • Given initial customer expectations, analyze the story results, and determine suggested improvements that can be presented to the customer.
  • Describe the process to perform writebacks to salesforce objects.

Sub-topic wise helpful documentation

  1. Data Prep
  2. Improvements
  3. Adjust Parameters
  4. Writeback

Exam Cheat Sheet

EINSTEIN ANALYTICSWHY IS IT SO GREAT?
Einstein Analytics PlusNative, actionable analytics embedded
right into Sales Cloud, Service Cloud, and
Salesforce Platform. Includes a visual data
prep layer to access, blend, and transform
Salesforce and non-Salesforce data, as well
as machine learning-based predictions
and recommendations.
Einstein Analytics for
Financial Services
Includes everything from Einstein Analytics
Plus, but is tailored with out-of-the-box
templates and KPIs for wealth, retail
banking, and insurance (pilot)
Einstein Analytics for
Manufacturing
Includes everything from Einstein Analytics
Plus, but is tailored with out-of-the-box
templates and KPIs for manufacturing
businesses
Einstein Analytics for
Communities
Drive sales through your distribution
channels by providing a complete,
AI-powered analytics solution that’s native
to Salesforce
Einstein Prediction BuilderWith automated machine learning (feature
selection, model management, model
metrics), create and deploy custom
predictions on any Salesforce object —
standard or custom — using clicks, not code
Einstein DiscoveryCreate and deploy custom machine
learning predictions using Salesforce and
non-Salesforce data with explanations of
business outcomes, recommendations
on how to improve, and transparent
model metrics
EINSTEIN PLATFORMWHY IS IT SO GREAT?
Einstein Object DetectionIdentify the quantity, size, and location
of objects within an image.
Einstein Image
Classification
Recognize and classify images specific to
your business, at scale
Einstein SentimentClassify the sentiment of unstructured
text into positive, negative, and
neutral classes
Einstein IntentCategorize the intended meaning of any
text into user-defined labels
EINSTEIN FOR SALES & SERVICEWHY IS IT SO GREAT?
Einstein Lead &
Opportunity Scoring
Prioritise the leads and opportunities
most likely to convert.
Einstein Account &
Opportunity Insights
Observe key developments and create
dashboards related to your accounts and
opportunities
Einstein Email InsightsPrioritise your inbox with
actionable intelligence
Einstein ForecastingEasily predict sales forecasts inside
of Salesforce
Einstein Activity Capture &
Einstein Automated Contacts
Automatically capture data and add new
contacts and events to your CRM
Einstein Recommended
Connections
Get insights about your team’s network
to see who knows your customers and
can help out on a deal
Einstein BotsAugment agents for
maximum productivity
Einstein Case ClassificationAutomatically populate or
recommend pick-list and
checkbox field values for
new cases
Einstein Next Best ActionDeliver optimal
recommendations at the point
of maximum impact to your
employees and customers

Link to the official documentation for updated information

Resources