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Data-Cloud-Consultant Practice Test Fee & Data-Cloud-Consultant Exams Dumps
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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:
Topic
Details
Topic 1
- Identity Resolution: It describes matching and how its rule sets are applied. Furthermore, it discusses reconciling data and its rule sets, the results of identity resolution, and use cases.
Topic 2
- Segmentation and Insights: This topic defines basic concepts of segmentation and use cases, identifies scenarios for analyzing segment membership, configuring, refining, and maintaining segments within Data Cloud, and differentiating between calculated and streaming insights.
Topic 3
- Data Cloud Setup and Administration: This topic includes applying Data Cloud permissions, permission sets, org-wide settings. It describes and configures data stream types, and data bundles. Moreover, it discusses use cases for data spaces, creating data spaces, managing and administering Data Cloud using reports, dashboards, flows, packaging, data kits, diagnosing and exploring data using Data Explorer, Profile Explorer, and APIs.
Topic 4
- Act on Data: This topic defines activations and their basic use cases, using attributes and related attributes, identifying and analyzing timing dependencies affecting the Data Cloud lifecycle. Additionally it focuses on troubleshooting common problems with activations, and using data actions, including their requirements and intended use cases.
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Salesforce Certified Data Cloud Consultant Sample Questions (Q134-Q139):
NEW QUESTION # 134
A consultant has an activation that is set to publish every 12 hours, but has discovered that updates to the data prior to activation are delayed by up to 24 hours.
Which two areas should a consultant review to troubleshoot this issue?
Choose 2 answers
- A. Review calculated insights to make sure they're run before segments are refreshed.
- B. Review calculated insights to make sure they're run after the segments are refreshed.
- C. Review segments to ensure they're refreshed after the data is ingested.
- D. Review data transformations to ensure they're run after calculated insights.
Answer: A,C
Explanation:
The correct answer is B and C because calculated insights and segments are both dependent on the data ingestion process. Calculated insights are derived from the data model objects and segments are subsets of data model objects that meet certain criteria. Therefore, both of them need to be updated after the data is ingested to reflect the latest changes. Data transformations are optional steps that can be applied to the data streams before they are mapped to the data model objects, so they are not relevant to the issue. Reviewing calculated insights to make sure they're run after the segments are refreshed (option D) is also incorrect because calculated insights are independent of segments and do not need to be refreshed after them. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Calculated Insights, Segments
NEW QUESTION # 135
Cloud Kicks received a Request to be Forgotten by a customer.
In which two ways should a consultant use Data Cloud to honor this request?
Choose 2 answers
- A. Add the Individual ID to a headerless file and use the delete from file functionality.
- B. Use Data Explorer to locate and manually remove the Individual.
- C. Delete the data from the incoming data stream and perform a full refresh.
- D. Use the Consent API to suppress processing and delete the Individual and related records from source data streams.
Answer: A,D
Explanation:
To honor a Request to be Forgotten by a customer, a consultant should use Data Cloud in two ways:
* Add the Individual ID to a headerless file and use the delete from file functionality. This option allows the consultant to delete multiple Individuals from Data Cloud by uploading a CSV file with their IDs1. The deletion process is asynchronous and can take up to 24 hours to complete1.
* Use the Consent API to suppress processing and delete the Individual and related records from source data streams. This option allows the consultant to submit a Data Deletion request for an Individual profile in Data Cloud using the Consent API2. A Data Deletion request deletes the specified Individual entity and any entities where a relationship has been defined between that entity's identifying attribute and the Individual ID attribute2. The deletion process is reprocessed at 30, 60, and 90 days to ensure a full deletion2. The other options are not correct because:
* Deleting the data from the incoming data stream and performing a full refresh will not delete the existing data in Data Cloud, only the new data from the source system3.
* Using Data Explorer to locate and manually remove the Individual will not delete the related records from the source data streams, only the Individual entity in Data Cloud. References:
* Delete Individuals from Data Cloud
* Requesting Data Deletion or Right to Be Forgotten
* Data Refresh for Data Cloud
* [Data Explorer]
NEW QUESTION # 136
A Data Cloud consultant recently discovered that their identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual.
What should the consultant do to address this issue?
- A. Modify the existing ruleset with stricter matching criteria, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.
- B. Create and run a new rules fewer matching rules, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
- C. Modify the existing ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
- D. Create and run a new ruleset with stricter matching criteria, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
Answer: D
Explanation:
Explanation
Identity resolution is the process of linking source profiles from different data sources into unified individual profiles based on match and reconciliation rules. If the identity resolution process is matching individuals that share email addresses or phone numbers, but are not actually the same individual, it means that the match rules are too loose and need to be refined. The best way to address this issue is to create and run a new ruleset with stricter matching criteria, such as adding more attributes or increasing the match score threshold. Then, the consultant can compare the two rulesets to review and verify the results, and see if the new ruleset reduces the false positives and improves the accuracy of the identity resolution. Once the new ruleset is approved, the consultant can migrate to the new ruleset and delete the old one. The other options are incorrect because modifying the existing ruleset can affect the existing unified profiles and cause data loss or inconsistency.
Creating and running a new ruleset with fewer matching rules can increase the false negatives and reduce the coverage of the identity resolution. References: Create Unified Individual Profiles, AI-based Identity Resolution: Linking Diverse Customer Data, Data Cloud Identiy Resolution.
NEW QUESTION # 137
Cumulus Financial uses Data Cloud to segment banking customers and activate them for direct mail via a Cloud File Storage activation. The company also wants to analyze individuals who have been in the segment within the last 2 years.
Which Data Cloud component allows for this?
- A. Nested segments
- B. Calculated insights
- C. Segment exclusion
- D. Segment membership data model object
Answer: D
Explanation:
The segment membership data model object is a Data Cloud component that allows for analyzing individuals who have been in a segment within a certain time period. The segment membership data model object is a table that stores the information about which individuals belong to which segments and when they were added or removed from the segments. This object can be used to create calculated insights, such as segment size, segment duration, segment overlap, or segment retention, that can help measure the effectiveness of segmentation and activation strategies. The segment membership data model object can also be used to create nested segments or segment exclusions based on the segment membership criteria, such as segment name, segment type, or segment date range. The other options are not correct because they are not Data Cloud components that allow for analyzing individuals who have been in a segment within the last 2 years. Nested segments and segment exclusions are features that allow for creating more complex segments based on existing segments, but they do not provide the historical data about segment membership. Calculated insights are custom metrics or measures that are derived from data model objects or data lake objects, but they do not store the segment membership information by themselves. Reference: Segment Membership Data Model Object, Create a Calculated Insight, Create a Nested Segment
NEW QUESTION # 138
What is the result of a segmentation criteria filtering on City | Is Equal To | 'San Jose'?
- A. Cities only containing 'San Jose' or 'San Jose'
- B. Cities only containing 'San Jose' or 'san jose'
- C. Cities containing 'San Jose', 'San Jose', 'san jose', or 'san jose'
- D. Cities only containing 'San Jose' or 'san jose'
Answer: B
Explanation:
The result of a segmentation criteria filtering on City | Is Equal To | 'San Jose' is cities only containing 'San Jose' or 'san jose'. This is because the segmentation criteria is case-sensitive and accent-sensitive, meaning that it will only match the exact value that is entered in the filter1. Therefore, cities containing 'San Jose', 'san jose', or 'San Jose' will not be included in the result, as they do not match the filter value exactly. To include cities with different variations of the name 'San Jose', you would need to use the OR operator and add multiple filter values, such as 'San Jose' OR 'San Jose' OR 'san jose' OR 'san jose'2. References: Segmentation Criteria, Segmentation Operators
NEW QUESTION # 139
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