QUESTION: 57

Northern Trail Outfitters (NTO) is configuring an identity resolution ruleset based on Fuzzy

Name and Normalized Email.
What should NTO do to ensure the best email address is activated?

Include Contact Point Email object Is Active field as a match rule.
Use the source priority order in activations to make sure a contact point from the desired source is delivered to the activation target.
Ensure Marketing Cloud is prioritized as the first data source in the Source Priority reconciliation rule.
Set the default reconciliation rule to Last Updated.

Answer(s): B

Explanation:

: NTO is using Fuzzy Name and Normalized Email as match rules to link together data from different sources into a unified individual profile. However, there might be cases where the same email address is available from more than one source, and NTO needs to decide which one to use for activation. For example, if Rachel has the same email address in Service Cloud and Marketing Cloud, but prefers to receive communications from NTO via Marketing Cloud, NTO needs to ensure that the email address from Marketing Cloud is activated. To do this, NTO can use the source priority order in activations, which allows them to rank the data sources in order of preference for activation. By placing Marketing Cloud higher than Service Cloud in the source priority order, NTO can make sure that the email address from Marketing Cloud is delivered to the activation target, such as an email campaign or a journey. This way, NTO can respect Rachel's preference and deliver a better customer experience.

Reference:

Configure Activations, Use Source Priority Order in Activations

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QUESTION: 58

A customer wants to create segments of users based on their Customer Lifetime Value. However, the source data that will be brought into Data Cloud does not include that key performance indicator (KPI).
Which sequence of steps should the consultant follow to achieve this requirement?

Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation
Create Calculated Insight > Map Data to Data Model> Ingest Data > Use in Segmentation
Create Calculated Insight > Ingest Data > Map Data to Data Model> Use in Segmentation
Ingest Data > Create Calculated Insight > Map Data to Data Model > Use in Segmentation

Answer(s): A

Explanation:

To create segments of users based on their Customer Lifetime Value (CLV), the sequence of steps that the consultant should follow is Ingest Data > Map Data to Data Model > Create Calculated Insight > Use in Segmentation. This is because the first step is to ingest the source data into Data Cloud using data streams. The second step is to map the source data to the data model, which defines the structure and attributes of the data. The third step is to create a calculated insight, which is a derived attribute that is computed based on the source or unified data. In this case, the calculated insight would be the CLV, which can be calculated using a formula or a query based on the sales order data. The fourth step is to use the calculated insight in segmentation, which is the process of creating groups of individuals or entities based on their attributes and behaviors. By using the CLV calculated insight, the consultant can segment the users by their predicted revenue from the lifespan of their relationship with the brand. The other options are incorrect because they do not follow the correct sequence of steps to achieve the requirement. Option B is incorrect because it is not possible to create a calculated insight before ingesting and mapping the data, as the calculated insight depends on the data model objects. Option C is incorrect because it is not possible to create a calculated insight before mapping the data, as the calculated insight depends on the data model objects. Option D is incorrect because it is not recommended to create a calculated insight before mapping the data, as the calculated insight may not reflect the correct data model structure and attributes.

Reference:

Data Streams Overview, Data Model Objects Overview, Calculated Insights

Overview, Calculating Customer Lifetime Value (CLV) With Salesforce, [Segmentation Overview]

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QUESTION: 59

During discovery, which feature should a consultant highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile?

Data Cleansing
Harmonization
Data Consolidation
Identity Resolution

Answer(s): D

Explanation:

Identity resolution is the feature that allows Data Cloud to match and reconcile data about individuals from multiple data sources into a single unified profile. Identity resolution uses rulesets to define how source profiles are matched and consolidated based on common attributes, such as name, email, phone, or party identifier. Identity resolution enables Data Cloud to create a 360- degree view of each customer across different data sources and systems. The other options are not the best features to highlight for this customer need because:
A . Data cleansing is the process of detecting and correcting errors or inconsistencies in data, such as duplicates, missing values, or invalid formats. Data cleansing can improve the quality and accuracy of data, but it does not match or reconcile data across different data sources. B . Harmonization is the process of standardizing and transforming data from different sources into a common format and structure. Harmonization can enable data integration and interoperability, but it does not match or reconcile data across different data sources. C . Data consolidation is the process of combining data from different sources into a single data set or system. Data consolidation can reduce data redundancy and complexity, but it does not match or reconcile data across different data sources.

Reference:

1: Data and Identity in Data Cloud | Salesforce Trailhead, 2: Data Cloud Identiy Resolution | Salesforce AI Research, 3: [Data Cleansing - Salesforce], 4: [Harmonization - Salesforce], 5: [Data Consolidation - Salesforce]

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QUESTION: 60

Northern Trail Outfitters (NTO) wants to send a promotional campaign for customers that have purchased within the past 6 months. The consultant created a segment to meet this requirement.
Now, NTO brings an additional requirement to suppress customers who have made purchases within the last week.
What should the consultant use to remove the recent customers?

Batch transforms
Segmentation exclude rules
Related attributes
Streaming insight

Answer(s): B

Explanation:

The consultant should use B. Segmentation exclude rules to remove the recent customers. Segmentation exclude rules are filters that can be applied to a segment to exclude records that meet certain criteria. The consultant can use segmentation exclude rules to exclude customers who have made purchases within the last week from the segment that contains customers who have purchased within the past 6 months. This way, the segment will only include customers who are eligible for the promotional campaign.
The other options are not correct. Option A is incorrect because batch transforms are data processing tasks that can be applied to data streams or data lake objects to modify or enrich the data. Batch transforms are not used for segmentation or activation. Option C is incorrect because related attributes are attributes that are derived from the relationships between data model objects. Related attributes are not used for excluding records from a segment. Option D is incorrect because streaming insights are derived attributes that are calculated at the time of data ingestion. Streaming insights are not used for excluding records from a segment.

Reference:

Salesforce Data Cloud Consultant Exam Guide, Segmentation, Segmentation Exclude Rules

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Free Data-Cloud-Consultant Exam Braindumps (page: 15)

QUESTION: 57

Explanation:

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QUESTION: 58

Explanation:

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QUESTION: 59

Explanation:

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QUESTION: 60

Explanation:

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