Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Dumps

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

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.

This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.

Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.

Go back to the data preparation and reduce the number of fields to less than 30 in order to produce a story.

Build a story with a first set of predictors and assess which predictors are important to the story. Then drop the less important ones and add the predictors that were omitted in the first run and assess their impact.

Rest API

Salesforce Managed Package

Change Sets

Metadata API

SalesPefformance_1

SalesPerformance_1 waveDashboard

SalesPerformance_1 EADashboard

SalesPerformance_1 EdgeOashboard

Storage rows of data

Salesforce external connector number of synced rows

Total number of enabled objects

Snowflake connector number synced rows

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.

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

Time

Geospatial

Statistical significance

Alphabetical

There is no order

The current user

The system administrator

The Integration User

The record owner

Existing datasets

Records from a Salesforce object

Dataset lens

Connected data(Synced)

Selecting a Dataset in Einstein Analytics, then clicking Create Story

Copying text from company reports and pasting into Einstein Discovery

Uploading a Microsoft® PowerPoint® presentation

Entering notes about the data

How combinations of factors affect the CLV for Naval customers

How individual factors separately affect the outcome for Naval customers

What the relationship is between significant and insignificant factors as they impact CLV for Naval customers

How the chosen variable is related to all other factors in the model for Naval customers

The average daily in SAN Francisco stores in November as 1601 items higher than the global average of 335.

November sales are higher than in other months. This November-effect is the strongest in San Francisco.

The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all other months in San Francisco.

The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all November sales in the country

The field does not contain any data in Salesforce.

The user who runs the dataflow does not have access to the field.

The "Integration User Profile" does not have access to the field.

The ""Security User Profile" does not have access to the field.

Automatically remove bottlenecks to make queries run faster.

Get a list of recommendations on how to improve the performance of the dashboard.

View all queries and the time it took to run each one.

View the total time required to run all queries.

See the final query for each step along with query results.

Extract data from the case and account objects using "sfdcDigest" transformation, Q use "append" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

Extract data from the case and account objects using an ETL tool, use the ETL tool to y/ Q join account and case data, and then upload the data to Einstein Analytics. Build a dashboard using the ETL generated dataset

Extract data from the case and account objects using "sfdcDigest" transformation, y/ Q use "augment" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

Extract data from the case and account objects using "dataloader", create a CSV file Q with account and case data, and then upload the CSV as a dataset to Einstein Analytics. Build a dashboard using that dataset

Download the XMD for the dataset and change the values and color for the fields and then upload to the dataset.

In the explorer, select the boolean fields and use the "edit values" option on the fields to change values and colors.

Create an XMD node in the dataflow to change the label and color of the values in the fields.

In the explorer, select the boolean fields, then create a derived dimension that references the boolean fields, and then modify the values and colors of the derived dimension.

Field audit trail data

Forensics data

Sales Cloud data Login

Event log file data

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.

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

edgemart

sfdcRegister

export

sfdcDigest

Selected variable with CLV

Selected variable average with the mean

Selected variable with a different variable

Selected variable average with the filtered average

Selected variable with all story data

The app, dataflows and datasets, dashboards, and permission sets

The app, dataflows and datasets, and dashboards

The app, dataflows and datasets, and users

An empty app

computeExpression

computeData

computeValue

computeField

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.

2

1

0

3

Is like having a personal data scientist on staff

Replaces your team of BI experts and data analysts

Helps you hire the best data scientist for your business

Understands your business better than you do

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.

Bar chart

Flat gauge

Pivot table

Pie chart

Manually add a flag to each row to prevent access.

Use a security predicate to filter which rows are returned.

Use subtle thought control.

Remove restricted rows from the JSON file.

Automatically creates multiple versions of the same app

Lets you know the best way to set up your Salesforce data

Asks you questions about how you store data in your org to help you create your app

Customizes your template based on Salesforce settings

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.

No, you can't share data from a widget.

Select the step of the widget and share data.

Select "Share" icon, click on "Download", and select "Download in .png, .XLS, or CSV" format.

Only with the proper Einstein Analytics Platform license.

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

Determine if the timeseries required parameter, prediction interval, should be 80 or 95.

Investigate which prediction model is best for the data available.

Validate that there is enough data in the dataset to make a good prediction.

Include the fill SAQL statement to fill any missing dates.

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');

An enterprise org, because that type of org is typically used by large companies

A trial org, because you don't need to save anything

A Developer Edition org, because it's a free, safe environment where you can try things out

A higher education org, because analytics requires advanced math

Aggregate by a measure

Count rows by measure you want to visualize.

Filter to narrow down your results.

Grouping by a dimesnsion

Display the results graphically

If a user can see more than 3,000 records on the object in Salesforce, but the user does not have the "View All Data" permission,

sharing inheritance is not used. The backup security predicate takes effect. This limitation does not apply to the Opportunity object.

Sharing inheritance supports Account, Campaign, Case, Contact, Opportunity, Lead, Order, User, and custom objects. If you use

other objects, such as Idea or Site, you must use security predicates for those objects.

A dataset using sharing must also have a security predicate defined.

Sharing inheritance supports a foreign key for enforcing security.

When sharing inheritance is enabled, security predicates are ignored.

It's not possible. Danielle will need to log into Einstein Discovery and import the data from there.

Danielle needs a permission set containing the Download Analytics Data permission, then she can use the Export node to load data into Einstein Discovery.

Danielle needs a permission set containing the Upload External Data to Analytics permission, then she can log into Einstein Discovery and import the data from there.

Data loaded into Einstein Analytics as datasets can then be used in Einstein Discovery without a separate load process.

Wait at least 3 days after purchase.

Have a Customer Community Plus or Partner Community license.

Be nominated by your community peers.

Assign your community members the Analytics for Communities permission set license.

B and D

Remove the outliers as suggested by Einstein and deploy the model.

Remove the outliers as suggested by Einstein, and verify using model metrics and story insights if the quality improved.

Discuss with the client if values below 0 and above 2,489 are so uncommon that they should perhaps be left out of the story.

Manually remove the sales bellow 0 (negative sales must be a data issue), but keep the large value (the more data, the better the model will be).

Germany performs lower than average but better when the product is Call Center.

The -2.2 Unexplained means there are effects that Einstein will never be able to explain regardless of the dataset.

Call Center is a generally poor performing product, and since there is more in Germany that has a negative effect.

Germany sells more Call Center product and that helps them increase their win rate.

Grant Benito access to all datasets in the org.

Assign Benito a permission set that contains the permission: Create and Edit Analytics Dashboards.

Grant Benito access to the app that contains the Seed Bank dataset and assign him a permission set that contains the Create and Edit Analytics Dashboards permission.

Assign Benito a permission set that contains the permission: Create and Manage Analytics Apps.

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.

Enable fast sync in analytics settings

Contact Salesforce support to Increase sync speed

Evaluate connection mode for each connected object

Merge synced objects

append

computeExpression

augment

computeRelative

Consult a Data Scientist for further analysis.

Determine if the pattern is a data issue or a new insight.

Filter out the data that causes the unexpected pattern and analyze the new results.

Accept the new pattern and have confidence that Einstein knows the business as accurately as the customer.

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.

Maximum number of dataflow definitions

Maximum number of dataflow transformations

Maximum number of objects that can be enabled for data sync (replication)

Timeout for ELT (Extract Load Transform) jobs that are scheduled but not yet executed

/services/data/v . /wave/apps

/services/data/v . /analytics/wizard

/services/data/v . /wave/templates

/services/data/v . /analytics/projects

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

Create two permission sets with different system permissions.

Use a permission set license with two different levels of access.

Grant "Manage" access permission to the apps.

Create a permission set license assignment with two different levels of access-

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

Replace nulls in the Churn Reason filed as ‘No reason given’’,

Exclude active customers (customer who have not churned) from the training dataset since their Churned) from the training dataset since their Churn Reason field are nulls.

For the Churn Reason field, enable the ‘’This variable contains sensitive data’’ box because it may contain sensitive customer behavior.

Exclude the Churn reason field from the dataset.

sfdcDigest

digest

export

edgemart

digest transformation

flatten transformation

delta transformation

sfdcDigest transformation

Use nested binding to update the grouping in the chart.

Use selection binding to update the measure in the chart.

Use result binding to update the measure in the chart.

Use selection binding to update the grouping in the chart.

Create 4 analytics lenses and embed them onto the account record page

Create datasets for account managers to explore

Create a dashboard and embed it onto the account record page with a filter based on the viewed account

Create a dashboard and embed onto the account record page

A fill statement

A cogrouop or union statement

A union statement

A cogroup statement

Mass edit all the records by populating the Predicted Amount field.

Use the Prediction Service API to get the prediction and populate the Predicted Amount field.

Enable a Bulk Scoring Job under the Prediction Definition.

Open all Opportunity records.

1500

1000

3000

2000

Retain Promotion; it is an actionable variable and without it, we cannot recommend prescriptive actions.

Cancel this story and redefine the dataset.

Retain Amount; not knowing the size of a deal makes it hard to predict if will be won.

Do nothing; they appear to be similar, but there might be differences from the business perspective.

In the connected user's currency

In the integration user's currency

In the currency that is set on the "currency" attribute in the dataset

In the currency that is set on the "currency" attribute in the dataflow

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.

Inherited security

Row-level security with security predicates

Field-level security

App level security

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.

200

100

150

50

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.

Third-order analysis

Object-oriented analysis

Second-order analysis

First-order analysis

Spectrum analysis