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Salesforce Certified Data Cloud Consultant Sample Questions (Q91-Q96):

NEW QUESTION # 91
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?

Answer: A

Explanation:
The feature that the consultant should highlight for a customer who has multiple data sources and needs to match and reconcile data about individuals into a single unified profile is D. Identity Resolution. Identity Resolution is the process of identifying, matching, and reconciling data about individuals across different data sources and creating a unified profile that represents a single view of the customer. Identity Resolution uses various methods and rules to determine the best match and reconciliation of data, such as deterministic matching, probabilistic matching, reconciliation rules, and identity graphs. Identity Resolution enables the customer to have a complete and accurate understanding of their customers and their interactions across different channels and touchpoints. References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution


NEW QUESTION # 92
An organization wants to enable users with the ability to identify and select text attributes from a picklist of options.
Which Data Cloud feature should help with this use case?

Answer: A

Explanation:
Value suggestion is a Data Cloud feature that allows users to see and select the possible values for a text field when creating segment filters. Value suggestion can be enabled or disabled for each data model object (DMO) field in the DMO record home. Value suggestion can help users to identify and select text attributes from a picklist of options, without having to type or remember the exact values. Value suggestion can also reduce errors and improve data quality by ensuring consistent and valid values for the segment filters. References: Use Value Suggestions in Segmentation, Considerations for Selecting Related Attributes


NEW QUESTION # 93
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers

Answer: C,D

Explanation:
A calculated insight is a custom metric or measure that is derived from one or more data model objects or data lake objects in Data Cloud. A calculated insight can be used in segmentation to filter or group the data based on the calculated value. However, not all calculated insights can appear in the segmentation canvas. There are two requirements that must be met for a calculated insight to appear in the segmentation canvas:
The calculated insight must contain a dimension including the Individual or Unified Individual Id. A dimension is a field that can be used to categorize or group the data, such as name, gender, or location. The Individual or Unified Individual Id is a unique identifier for each individual profile in Data Cloud. The calculated insight must include this dimension to link the calculated value to the individual profile and to enable segmentation based on the individual profile attributes.
The primary key of the segmented table must be a dimension in the calculated insight. The primary key is a field that uniquely identifies each record in a table. The segmented table is the table that contains the data that is being segmented, such as the Customer or the Order table. The calculated insight must include the primary key of the segmented table as a dimension to ensure that the calculated value is associated with the correct record in the segmented table and to avoid duplication or inconsistency in the segmentation results.
Create a Calculated Insight, Use Insights in Data Cloud, Segmentation


NEW QUESTION # 94
A customer notices that their consolidation rate has recently increased. They contact the consultant to ask why.
What are two likely explanations for the increase?
Choose 2 answers

Answer: B,D

Explanation:
The consolidation rate is a metric that measures the amount by which source profiles are combined to produce unified profiles in Data Cloud, calculated as 1 - (number of unified profiles / number of source profiles). A higher consolidation rate means that more source profiles are matched and merged into fewer unified profiles, while a lower consolidation rate means that fewer source profiles are matched and more unified profiles are created. There are two likely explanations for why the consolidation rate has recently increased for a customer:
New data sources have been added to Data Cloud that largely overlap with the existing profiles. This means that the new data sources contain many profiles that are similar or identical to the profiles from the existing data sources. For example, if a customer adds a new CRM system that has the same customer records as their old CRM system, the new data source will overlap with the existing one. When Data Cloud ingests the new data source, it will use the identity resolution ruleset to match and merge the overlapping profiles into unified profiles, resulting in a higher consolidation rate.
Identity resolution rules have been added to the ruleset to increase the number of matched profiles. This means that the customer has modified their identity resolution ruleset to include more match rules or more match criteria that can identify more profiles as belonging to the same individual. For example, if a customer adds a match rule that matches profiles based on email address and phone number, instead of just email address, the ruleset will be able to match more profiles that have the same email address and phone number, resulting in a higher consolidation rate.
Identity Resolution Calculated Insight: Consolidation Rates for Unified Profiles, Configure Identity Resolution Rulesets


NEW QUESTION # 95
A consultant is reviewing a recent activation using engagement-based related attributes but is not seeing any related attributes in their payload for the majority of their segment members.
Which two areas should the consultant review to help troubleshoot this issue?
Choose 2 answers

Answer: C,D

Explanation:
Engagement-based related attributes are attributes that describe the interactions of a person with an email message, such as opens, clicks, unsubscribes, etc. These attributes are stored in the Engagement data model object (DMO) and can be added to an activation to send more personalized communications. However, there are some considerations and limitations when using engagement-based related attributes, such as:
For engagement data, activation supports a 90-day lookback window. This means that only the attributes from the engagement events that occurred within the last 90 days are considered for activation. Any records outside of this window are not included in the activation payload. Therefore, the consultant should review the event time of the related engagement events and make sure they are within the lookback window.
The correct path to the related attributes must be selected for the activation. A path is a sequence of DMOs that are connected by relationships in the data model. For example, the path from Individual to Engagement is Individual -> Email -> Engagement. The path determines which related attributes are available for activation and how they are filtered. Therefore, the consultant should review the path selection and make sure it matches the desired related attributes and filters.
The other two options are not relevant for this issue. The activations can reference segments that segment on profile data rather than engagement data, as long as the activation target supports related attributes. The activated profiles do not need to have a Unified Contact Point, which is a unique identifier for a person across different data sources, to activate engagement-based related attributes. References: Add Related Attributes to an Activation, Related Attributes in Data Cloud activation have no values, Explore the Engagement Data Model Object


NEW QUESTION # 96
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