Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant(SP23) Questions and Answers

Create a recipe to generate a new dataset from the "OpportunityWithAccount" and "CaseWithAccount" datasets aggregated by Account, and use the generated dataset in a lens.

Use "Compare Table" to join the "OpportunityWithAccount" and "CaseWithAccount" datasets using the "Accountld" and then aggregate by Account.

Set up Connect Data Sources in the UI between the "OpportunityWithAccount" and "CaseWithAccount" datasets.

Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results.

Rest API

Salesforce Managed Package

Change Sets

Metadata API

45

35

25

30

Calculation

Column Color

Format

Length

Column Name

Rerun and update the story with a different algorithm.

Proceed with deployment if the model quality metric values ire sufficient.

Edit the model accuracy settings and rerun the story.

Identify additional data that may have a stronger relationship with the outcome variable.

Filter and order can be interchanged. Offset must be after filter/order and limit must come after offset.

Use a dataflow to rename the stages with a preceding number.

Use a dashboard JSON to rename stages.

Use a dataflow to sort the stages by their internal name.

Use a dataset XMD to rename stages.

Treemap chart

Scatter chart

Metric Radar chart

Stacked Bar chart

Dataflow Editor

Recipes Editor

Step Editor

Settings

Assign the Editor role to the user.

Assign the Viewer role to the user.

Ensure the user has the Use Analytics permission.

Assign the Manager role to the user.

Assign the Manager role the user

Ensure the user has the Analytics permission

Assign the Editor role the user

Assign the view role to the user

An application built on top of Salesforce

A tool for analyzing surf reports and forecasts

A CRM alternative to Salesforce

An online repository of infographics

20 column minimum, 50 maximum

10 column minimum, 200 maximum

2 column minimum, 50 maximum

2 column minimum, 25 maximum

Results binding

Data bindings

Description bindings

Selections bindings

Change a field's name

Change a field's format

Change a field's type

All of the above

Each user has a corresponding Profile assigned (Analytics Cloud Integration User and Analytics Cloud Security User profiles).

When you query a dataset that has row-level security based on the User object, Analytics uses the permissions of the Security User to access the User object and its fields.

Analytics uses the permissions of the Integration User to extract data from Salesforce objects and fields when a dataflow job runs.

Set faceting to All instead of None in the query “Step_pie_1.”

Use result binding/interaction in the filters section of the query “Step_pie_1.”

Set faceting to all instead of None in the query “Region_1.”

Use selection binding/interaction in the filters section of the query “Region_1.”

Analytics permission set license (PSL)

Permission set with at least one Analytics user permission

Username and password

A and B

B and C

Modify the XMD so all labels and values are user-friendly.

Rename all the steps to correspond with the defined naming convention.

Remove the redundant steps to ensure dashboard performance.

Apply conditional formatting on the new charts.

Click the Actions bar from a bubble chart

Hover over the case number column of the values table widget and click the arrow that appears

Double-click the case number in the Actions widget

Simply @mention the case manager for that segment

Yes, the number of recipe runs does not exceed the limit of 60 in a 24 hour rolling period.

No, the number of recipe runs exceed the limit of 40 in a 24 hour rolling period.

No, the total duration of the recipe jobs exceeds one hour.

Yes, with the concurrent recipe runs the duration is less than hour.

Update

Transform

Append

join

Predicting outcomes that you don't actually have the right data for

Exploring your existing data to see what already happened

Drilling down into the underlying reasons behind a prediction

Choosing between all possible outcomes for a single variable

None of the data

Only rows designated by the Salesforce administrator

All rows and fields in the datasets

Only rows designated by the App Manager

Enable fast sync in analytics settings

Contact Salesforce support to Increase sync speed

Evaluate connection mode for each connected object

Merge synced objects

Emphasis: make sure they have space for important information, such as headlines and key charts.

Clarity: make sure they are uncluttered and easy to interpret.

Consistency: make sure they have a sense of familiarity to strengthen your users' ease of use.

Balance: make sure they have a balance of different charts to get a more interesting design.

Einstein Analytics Platform Admin

Einstein Analytics Platform User

Einstein Analytics

Einstein Analytics Superadmin

Identify data types and define the appropriate data as numeric.

Analyze data columns and decide which, if any, to drop.

Produce a graph of the data to analyze.

Load the data into Einstein Analytics

35

55

25

15

SalesPefformance_1

SalesPerformance_1 waveDashboard

SalesPerformance_1 EADashboard

SalesPerformance_1 EdgeOashboard

The list, chart, and table steps must share a common name.

The list, chart, and table steps must share common dimensions from different datasets.

The list, chart, and table steps must share the same dataset.

Chart and table steps must have their own list widgets.

Update contents in the Sales Operations app and changes will be pushed down to its generated apps.

Ensure the Sales team has the necessary permissions to customize their apps.

Create a configuration wizard for the app.

Update contents in the Sales Operations app and create new template versions of the app.

Look up the API name of the filtering field

Look up the API name of the source field

Find source and target step names

Configure the results binding on the target step in the dashboard JSON.

The Salesforce profile for the VP does not have read permission on some fields of the Opportunity__c custom object

The Security Predicates set up at the dataset level are preventing the VP from seeing data

The dataset is inheriting sharing from Salesforce and the VP can see more than 3000 rows

The Salesforce profile for the VP does not have read permission on the Opportunity__c custom object

Analytics permission set license (PSL)

Permission set with at least one Analytics user permission

Username and password

A and B

B and C

The root object determines who can see the dataset.

Only data from the root object can be included in the dataset.

The root object determines what related objects you can add to the dataset.

The root object determines what app the dataset is saved in.

Segment the account data per region and deploy the same model to all segments.

Deploy the same model to all accounts and set the region field as an actionable variable.

Deploy the same model to all accounts and use an Apex trigger to call the appropriate prediction.

Segment the account data per region and deploy the appropriate model for each segment.

Deploy the story, because the variation in the metrics is within the normal range.

Confirm that the overall GINI coefficient is good prior to deploying the story.

Research and check the dataset for outliers in the target field and the main predictors that are shown on top of the story.

Do not deploy the story immediately, and research why one fold is performing worse than the others.

A specialized version of the Einstein Analytics platform that runs outside of Salesforce

Analytics for iPad

A collection of datasets, dashboards, lenses, and a dataflow

The blueprint for your org's Einstein Analytics implementation

Show the customer how to export and review the R-Code model validation results

Check the p-values and standard deviation

Use the Share and Export feature to help the customer determine if the findings make logical sense

Consult with a Data Scientist to validate the findings

Use conditional formatting under the bar chart widget properties by selecting the dimension.

Use conditional formatting under the bar chart widget properties by selecting the measure.

Use bindings to specify a color per bar.

Create a compare table formula to specify a hidden metric for coloring purposes.

Line over Scatter Plot

Grid over Bar

Line over Bar

Line over Stacked Bar

q = cogroup Opportunity by 'Id', Activities by 'Opportunityld';

q = cogroup Opportunity by 'Id', Activities by 'Opportunityld' left;

q = cogroup Opportunity by 'Id' right, Activities by 'Opportunityld';

q = cogroup Opportunity by 'Id' left, Activities by 'Opportunityld';

Add Custom Domain

Filter by Range

Add Custom Range

Add Filter

Bar chart

Flat gauge

Pivot table

Pie chart

Use 'Connect Data Sources" and create a connection to connect the two datasets.

Use selection binding in the filters section of Che step "Step_pie_2."

Use "Connect Data Sources" and create a connection to connect the two widgets.

Use selection binding in the filters section of the step "Type_l."

200

500

100

50

You create a text-only version of an app.

The template will not include a configuration wizard.

The dataflow fails.

The app includes all possible dashboards by default.

B and D

q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);

q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');

q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);

q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');

Alphabetical, in ascending order

Alphabetical, in descending order

Insights that explain the most variation in the outcome variable, in ascending order

Insights that explain the most variation in the outcome variable, in descending order

B and D

It instantly provides key metrics on open cases, average time to close, first contact resolution, and customer satisfaction.

It lets you drill down to more detailed dashboards, like agent performance, channel review, and telephony metrics.

It's available on desktop and mobile.

It's a great place to start your analysis.

All of the above.

Save the dataset under "My Private App" so only a subset of selected users can access the dataset.

Use a security predicate in the dataset so users can see only rows belonging to their respective regions.

Create a sharing rule in Salesforce based on the Region field in the user object and set the dataset to inherit sharing from Salesforce.

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.

Explanatory variable

Outcome variable

Global variable

Local variable

Tertiary variable

To remove column headers

To make sure the content is interesting

To add currency symbols to numeric fields

To make sure Analytics can set the field type correctly

periodic Incremental Sync

Full Sync

Periodic Full Sync

Incremental Sync

Answer only questions that involve custom objects

Try each option in the dropdown one at a time

Download the app and run a SAQL query on the JSON

Click "Looks good, next" and look for questions highlighted in red

Representing the number of widgets on a dashboard

Filtering all related widgets in a dashboard by your selection

Something only programmers can access

Measuring the number of significant digits of precision for a particular metric

Choosing the color of the background in a graph

Case duration

Customer geography

Customer satisfaction

Case resolution

All of the above

Reorganize the dashboard widgets.

Distribute steps among separate pages.

Use SAQL code to join datasets at runtime.

Replace separate step filters with a global filter.

Move calculations to a dataflow.

Dataflow

Microsoft Azure SQL Data Warehouse Connector

Einstein Analytics Connector for Excel

Recipes

Analytics REST API

Segment the training set.

Change the order of the story insights to descending.

Run detect sentiment on the dataflow before creating the story.

Add more rows of relevant data.

What Changed Over Time

How Can I Improve It

Predictions & Improvements

Why It Happened

What Happened

False

True

It's a read-only file with all the user documentation for a template.

It's the boss of the template with all the information needed to create the app.

It makes sure that the app includes all the right Salesforce data.

It's automatically populated every time you create an app from the template.

Gauge

Line

Metric Radar

Heatmap

Create one application for each department and use roles to share applications.

Create one application for each department, put common datasets in the shared app, and use profiles to share applications.

Create one application for each department, put common datasets in the shared app, and use roles to share applications.

Create one application for each department, put common datasets in the shared app, and use permission sets to share applications.

Create a trigger on Opportunity and use the Salesforce External Connector to get predictions from Einstein Discovery.

Create an Apex batch on Opportunity and use the REST API to get predictions from Einstein Discovery.

Create a trigger on Opportunity and install the Einstein Discovery Writeback managed package from the AppExchange.

Create a trigger on Opportunity and use the REST API to get predictions from Einstein Discovery.

Import the CSV file into Einstein Discovery.

Import the CSV file into the most similar Salesforce object, then run reports on that object.

You can't import data from a CSV file

Import the CSV file into an Einstein Analytics dataset, then create a story.

Run and monitor

Plan and map

Extract and prepare

Build and explore