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Data-Cloud-Consultant Test Pattern, Data-Cloud-Consultant Vce File

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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.

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

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 5

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 (Q95-Q100):

NEW QUESTION # 95
A consultant is ingesting a list of employees from their human resources database that they want to segment on.
Which data stream category should the consultant choose when ingesting this data?

A. Profile Data
B. Contact Data
C. Other Data
D. Engagement Data

Answer: C

Explanation:
Categories of Data Streams:
* Profile Data: Customer profiles and demographic information.
* Contact Data: Contact points like email and phone numbers.
* Other Data: Miscellaneous data that doesn't fit into the other categories.
* Engagement Data: Interactions and behavioral data.

NEW QUESTION # 96
Northern Trail Outfitters (NTD) creates a calculated insight to compute recency, frequency, monetary {RFM) scores on its unified individuals. NTO then creates a segment based on these scores that it activates to a Marketing Cloud activation target.
Which two actions are required when configuring the activation?
Choose 2 answers

A. Choose a segment.
B. Select contact points.
C. Add the calculated insight in the activation.
D. Add additional attributes.

Answer: A,B

NEW QUESTION # 97
Northern Trail Outfitters wants to use some of its Marketing Cloud data in Data Cloud.
Which engagement channel data will require custom integration?

A. Email
B. CloudPage
C. Mobile push
D. SMS

Answer: B

Explanation:
CloudPage is a web page that can be personalized and hosted by Marketing Cloud. It is not one of the standard engagement channels that Data Cloud supports out of the box. To use CloudPage data in Data Cloud, a custom integration is required. The other engagement channels (SMS, email, and mobile push) are supported by Data Cloud and can be integrated using the Marketing Cloud Connector or the Marketing Cloud API. Reference: Data Cloud Overview, Marketing Cloud Connector, Marketing Cloud API

NEW QUESTION # 98
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?

A. Identity Resolution
B. Data Consolidation
C. Harmonization
D. Data Cleansing

Answer: A

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 systems12. 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 sources3.
* 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 sources4.
* 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 sources5. References: 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]

NEW QUESTION # 99
A segment fails to refresh with the error "Segment references too many data lake objects (DLOS)".
Which two troubleshooting tips should help remedy this issue?
Choose 2 answers

A. Use calculated insights in order to reduce the complexity of the segmentation query.
B. Split the segment into smaller segments.
C. Space out the segment schedules to reduce DLO load.
D. Refine segmentation criteria to limit up to five custom data model objects (DMOs).

Answer: A,B

Explanation:
The error "Segment references too many data lake objects (DLOs)" occurs when a segment query exceeds the limit of 50 DLOs that can be referenced in a single query. This can happen when the segment has too many filters, nested segments, or exclusion criteria that involve different DLOs. To remedy this issue, the consultant can try the following troubleshooting tips:
* Split the segment into smaller segments. The consultant can divide the segment into multiple segments that have fewer filters, nested segments, or exclusion criteria. This can reduce the number of DLOs that are referenced in each segment query and avoid the error. The consultant can then use the smaller segments as nested segments in a larger segment, or activate them separately.
* Use calculated insights in order to reduce the complexity of the segmentation query. The consultant can create calculated insights that are derived from existing data using formulas. Calculated insights can simplify the segmentation query by replacing multiple filters or nested segments with a single attribute.
For example, instead of using multiple filters to segment individuals based on their purchase history, the consultant can create a calculated insight that calculates the lifetime value of each individual and use that as a filter.
The other options are not troubleshooting tips that can help remedy this issue. Refining segmentation criteria to limit up to five custom data model objects (DMOs) is not a valid option, as the limit of 50 DLOs applies to both standard and custom DMOs. Spacing out the segment schedules to reduce DLO load is not a valid option, as the error is not related to the DLO load, but to the segment query complexity.
References:
* Troubleshoot Segment Errors
* Create a Calculated Insight
* Create a Segment in Data Cloud

NEW QUESTION # 100
......

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Doug Ward Doug Ward

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