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A training dataset Is being prepared for a Einstein Discovery story. One skewed with outliers. at action should the Einstein consultant take?
A. Remove the outlier rows.
B. Nothing, because the field is not the outcome variable.
C. Change the method of binning to fixed width.
D. Remove the field because it has bad data
A consultant has been brought in to help increase the adaption of Tableau CRM. It has been identified that account managers are struggling to get an overview of their accounts before meeting with them. Ideally they would like to have key details highlighted about active cases and opportunities, opportunity value and product whitespace. What can the consultant do to increase adoption?
A. Create 4 analytics lenses and embed them onto the account record page
B. Create datasets for account managers to explore
C. Create a dashboard and embed it onto the account record page with a filter based on the viewed account
D. Create a dashboard and embed onto the account record page
A data architect wants to use a dataflow transformation to implement row-level security that is based on role hierarchy in Salesforce. Which transformation should be used to level the dataset hierarch?
A. digest transformation
B. flatten transformation
C. delta transformation
D. sfdcDigest transformation
To model customer value, a consultant decided to aggregate the amount ($) individual customers spent over a 2-year span. With reference to the outcome variable, which action should the consultant take?
A. Create five bins of revenues, ranging from Very High Value, high Value, average Value,
Low Value, and very Low Value in dataflow.
B. Select the option: Are you expecting a whole number greater than or equal to 0?
C. Create five bins or revenue, ranging from very high Value, High Value, Average Value, Low Value, and Very Low Value in data prep.
D. Select to minimize the outcome variable.
Your sales team requests that datasets for their dashboards are refreshed every hour. You agree to investigate if this is possible and find that the dashboards use A datasets created from two recipes. The first recipe takes 43 min to run and the second recipe takes 25 min to run. Is it possible to refresh data every hour?
A. Yes, the number of recipe runs does not exceed the limit of 60 in a 24 hour rolling
period.
B. No, the number of recipe runs exceed the limit of 40 in a 24 hour rolling period.
C. No, the total duration of the recipe jobs exceeds one hour.
D. Yes, with the concurrent recipe runs the duration is less than hour.
Universal Containers reports that any selection in the List widget is not affecting the Pie
chart in one of their Einstein Analytics dashboards. The step options associated with the
List widget and Pie chart are shown in the graphic.
Given that the steps are using different datasets. which two changes can an Einstein
Consultant make to solve this issue' Choose 2 answers
A. Use 'Connect Data Sources" and create a connection to connect the two datasets.
B. Use selection binding in the filters section of Che step "Step_pie_2."
C. Use "Connect Data Sources" and create a connection to connect the two widgets.
D. Use selection binding in the filters section of the step "Type_l."
A consultant has created a story to maximize the daily sales quantity of consumer products in stores. After creating a story, the consulting is presented with this data alert by Einstein Discovery (see graphic). What are two appropriate actions to take? Choose 2 answers
A. Remove the outliers as suggested by Einstein and deploy the model.
B. Remove the outliers as suggested by Einstein, and verify using model metrics and story insights if the quality improved.
C. 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.
D. 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).
A Tableau CRM consultant has created three recipes that each produce a dataset. Recipe1 creates Dataset1. Recipe2 created Dataset2. Recipe2 created Dataset3. Recipe3 is dependent on Dataset1, while Recipe1 and Recipe2 have no dependencies. How should the consultant implement the recipe schedule?
A. Make Recipe1 and Recipe2 time based, but make Recipe3 event based when Recipe1
has successfully completed.
B. Make Recipe1 and Recipe2 time based, but make Recipe3 event based when Recipe1 and Recipe2 have successfully completed.
C. Make Recipe1 and Recipe2 time based, but make Recipe3 event based when the local Salesforce connector has completed.
D. Make all three recipe time based and schedule all of them at the same time, the data manage will automatically run them in the correct order.
Which three things can be done with the tableau CRM Dashboard Inspector? Choose 3 answers
A. Get a list of recommendations on how to improve the performance of the dashboard.
B. View the total time required to run all queries.
C. Automatically remove bottlenecks to make queries run faster.
D. View all queries and the time it took to run each one.
E. See the final query for each query along with query results.
A consultant is tasked with creating one query that shows how many opportunities and cases there are per account. Cases and opportunities are found in two different datasets with a reference to the related Account via the AccoundId. What options do the consultant have in Analytics Studio to create the query?
A. A fill statement
B. A cogrouop or union statement
C. A union statement
D. A cogroup statement
As a tableau CRM consultant you have been asked to bring data from a Snowflake
database as well external Salesforce environment into Tableau CRM (CRM Analytics Plus
edition). 25 objects have been enabled from the local Salesforce connector. After further
investigation of the external Salesforce environment and 15 object each from the remaining
2 external Salesforce environment. You estimate each connector will per object bring
between 1000 and 1 mil rows of data.
What limit will be exceeded?
A. Storage rows of data
B. Salesforce external connector number of synced rows
C. Total number of enabled objects
D. Snowflake connector number synced rows
Which set of statements generates monthly amount on a cumulative basis annually?
A. Option A
B. Option B
C. Option C
D. Option D
After the initial creation of a story, the first story insight explains 93% of the variation of the outcome variable. This is unusual high? What is the most likely multiple for this?
A. The dataset contains multiple dominant values.
B. The dataset contains too many rows.
C. The dataset used in the story suffers from too many outlier values.
D. The outcome variable is causing data leakage.
A consultant built a very useful Einstein Analytics app for the corporate Sales Operations team of a company that has multiple Salesforce Ogrs, Each Org has similar models and would like to review their data the way that the corporate Sales Operations team does. The app has been packaged and needs to be installed on the other production orgs. What is the recommended practice to install the custom template app?
A. Rest API
B. Salesforce Managed Package
C. Change Sets
D. Metadata API
A customer has a dataset consisting of over 300 unique product names. They request a prediction model with the product names included. Which action should the Einstein Consultant take?
A. Split the analysis into multiple models will each having fewer products
B. Adjust the model to eliminate extreme values in the outcome variable.
C. Run the model using the default variables in the Product object
D. Use SKU numbers rather than product names to increase clarity.
Universal Containers reports that nay selection in the List widget is not affecting the pie
chart in one of their tableau CRM dashboard. They query options associated with the List
widget and Pie chart are shown in the graphic
Which change can a Tableau CRM Consultant implement to solve this issues, given that
the queries are using the same dataset?
A. Set faceting to All instead of None in the query “Step_pie_1.”
B. Use result binding/interaction in the filters section of the query “Step_pie_1.”
C. Set faceting to all instead of None in the query “Region_1.”
D. Use selection binding/interaction in the filters section of the query “Region_1.”
A Tableau CRM consultant has just completed deployment of an analytic app containing a recipe plus several datasets and dashboards. While conducting post deployment a smoke test, the new datasets don't seem to have migrated. What post migration step has likely been forgotten? What post migration step has likely been forgotten?
A. Apply security predicates on datasets
B. Run the recipe
C. Provide read access on the datasets
D. Go to Analytics Settings in Setup and approve the deployment
A customer is reviewing a story that is set to maximize the daily sales quantity of consumer products in stores and sees this chart. The visualized tooltip belongs to the blue bar for San Francisco, reflecting, November daily sales quantities in that city specifically. What two conclusions can be drawn from this insight?
A. The average daily in SAN Francisco stores in November as 1601 items higher than the
global average of 335.
B. November sales are higher than in other months. This November-effect is the strongest in San Francisco.
C. The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all other months in San Francisco.
D. 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 Universal Containers Einstein Analytics team built a dashboard with two widgets:
1. List widget associated to the step "Type_2" and grouped by the dimension "Type" (multiselection)
2. Pie chart widget associated to the step "Step_pie_3" and grouped by the dimension
"Type"
The team wants to use bindings so any selection in the List widget will filter the Pie chart.
Additional notes:
* The steps use different datasets.
* Users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding?
A. Option A
B. Option B
C. Option C
D. Option D
The Tableau CRM team at a company creates three recipes. 1) myRecipeOne: this recipe takes 2 hours to run. 2) mvRedpeTwo: this recipe takes 1 hour and 30 minutes to run. 3) myRecipeThree: this recipe takes 1 minute and 30 seconds to run. If all three recipes run, how many count towards the 24 hour rolling recipe run limit.c
A. 2
B. 1
C. 0
D. 3
Refer to the graphic.
Einstein found a recommendation to improve the story: apparently there are two variables
that behave the same. Given there is no additional information, what is the correct action?
A. Retain Promotion; it is an actionable variable and without it, we cannot recommend
prescriptive actions.
B. Cancel this story and redefine the dataset.
C. Retain Amount; not knowing the size of a deal makes it hard to predict if will be won.
D. Do nothing; they appear to be similar, but there might be differences from the business perspective.
A story has been deployed to the Opportunity object with a prediction field, Predicted Amount. How can all the Opportunity records be predictions written back to the Predicted Amount field?
A. Mass edit all the records by populating the Predicted Amount field.
B. Use the Prediction Service API to get the prediction and populate the Predicted Amount field.
C. Enable a Bulk Scoring Job under the Prediction Definition.
D. Open all Opportunity records.
A customer is reviewing a story that is set to maximize the daily sales quantity of consumer products in stores, and the customer sees chart related to promotional activities and to San Francisco specifically. What conclusion can be drawn from this insight? What conclusion can be drawn from this Insight?
A. Of all promotions types in San Francisco, sales were the highest using Display
promotion. play.
B. Promotions increase sales stronger in San Francisco than they do in other cities.
C. The best promotion type is Dis
D. The Other stores (visualized with the gray bars) show no significant data for the sales
promotions.
An Einstein Analytics Consultant is working with a subscriptions based company to build a dashboard to understand customer renewals. Each subscription is captured as a Closed Won Opportunity within Salesforce Unfortunately the Opportunity record does not specify whether it is a renewal or a net new subscription. Which data transformation should be used to determine if a subscription is new or a renewal?
A. ComputeRelative
B. ComputeExpression
C. Augment
D. flatten
A large company is rolling out Einstein Analytics to their field sales. They have a welldefined role hierarchy where everyone is assigned to an appropriate node on the hierarchy. An individual Sales rep should be able to view all opportunities that she/he owns or as part of the account team or opportunity team. The Sales Manager should be able to view all opportunities for the entire Sales team. Similarly, the Sales Vice President should be able to view opportunities for everyone who rolls up in that hierarchy. The opportunity dataset has a field called 'Ownerld' which represents the opportunity owner. Given this information, how can an Einstein Consultant implement the above requirements?
A. As part of the dataflow, use the flatten operation on the role hierarchy and create a
multivalue attribute called 'ParentRolelDs' on the
opportunity dataset and apply following security predicate: 'ParentRolelDs' ==
"$User.UserRoleId" && 'Ownerld' == "SUser.Id".
B. As part of the dataflow, use computeExpression on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".
C. As part of the dataflow, use computeRelative on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".
D. As part of the dataflow, use the flatten operation on the role hierarchy and create a
multivalue attribute called 'ParentRolelDs' on the
opportunity dataset and apply following security predicate: 'ParentRolelDs' ==
"$User.UserRoleId" || TeamMember.Id' == "$User. Id" || 'Ownerld' == "SUser.Id".
Results from an Einstein Discovary story are reviewed with a business user. They agree with the findings but noticed that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model may not be good enough to next steps?
A. Rerun and update the story with a different algorithm.
B. Proceed with deployment if the model quality metric values ire sufficient.
C. Edit the model accuracy settings and rerun the story.
D. Identify additional data that may have a stronger relationship with the outcome variable.
A consultant built a Tableau CRM dashboard for a shipping company. The consultant enabled data sync (replication) to increase the speed to datasets refreshing. How often will the data on the dashboard be refreshed?
A. When the dashboard viewer clicks the Refresh button.
B. When the Data Sync runs to completion, and then recipe runs to completion.
C. When recipe runs to completion, and then Data Sync runs to completion.
D. Each time a user opens the dashboard
The client is trying to create a SAQL step to predict sales in each sales region. They cannot
get the query to return any results, but have identified that the error is in the time series
statement. They have asked an Einstein Consultant to review the following query and fix
any errors.
Which timeseries statement will fix the query'
A. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with
(dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);
B. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');
C. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);
D. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');
When you assign any Analytics permission set to users in your org, Salesforce autoassigns the Analytics Platform permission set license to that user
A. True
B. False
A company asks a Tableau CRM consultant to review the performance of their local data sync. After removing unused objects and fields from connected data, what else can the consultant do to improve performance of data sync"*
A. Enable fast sync in analytics settings
B. Contact Salesforce support to Increase sync speed
C. Evaluate connection mode for each connected object
D. Merge synced objects
Universal Containers (UC) is a multinational company that utilizes Salesforce and has a variety of internal systems. UC uses Einstein Analytics for their data analysis platform and they want to automate their weekly manual process to create a dataset from their onpremise data warehouse. Which solution should a consultant recommend to meet this requirement?
A. Utilize a Salesforce weekly export feature
B. Utilize Analytics Connector.
C. Utilize middleware with Analytics External Data API
D. Utilize a dataflow
A. Alphabetical, in ascending order
B. Alphabetical, in descending order
C. Insights that explain the most variation in the outcome variable, in ascending order
D. Insights that explain the most variation in theoutcome variable, in descending order
E. B and D
A. edgemart
B. sfdcRegister
C. export
D. sfdcDigest
A. q = cogroup Opportunity by 'Id', Activities by 'Opportunityld';
B. q = cogroup Opportunity by 'Id', Activities by 'Opportunityld' left;
C. q = cogroup Opportunity by 'Id' right, Activities by 'Opportunityld';
D. q =cogroup Opportunity by 'Id' left, Activities by 'Opportunityld';
A. Is like having a personal data scientist on staff
B. Replaces your team of BI experts and data analysts
C. Helps you hire the best data scientist for your business
D. Understands your business better than you do
A. ui.json
B. template-to-app-rules.json
C. aster- info.j son
D. template-info.json
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