Rob King Rob King's Profile Page

Rob King Rob King

0 Course Enrolled • 0 Course Completed

Biography

Data-Cloud-Consultant Valid Dumps Pdf | New Data-Cloud-Consultant Exam Name

BTW, DOWNLOAD part of PrepAwayETE Data-Cloud-Consultant dumps from Cloud Storage: https://drive.google.com/open?id=1SxJQyVIRyXcWvOT9Wkv0uA348qJGmMyO

The Salesforce Certified Data Cloud Consultant (Data-Cloud-Consultant) exam questions are real, valid, and verified by Salesforce Data-Cloud-Consultant certification exam trainers. They work together and put all their efforts to ensure the top standard and relevancy of Data-Cloud-Consultant Exam Dumps all the time. So we can say that with Salesforce Data-Cloud-Consultant exam questions you will get everything that you need to make the Data-Cloud-Consultant exam preparation simple, smart, and successful.

Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.

Topic 2

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 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.

>> Data-Cloud-Consultant Valid Dumps Pdf <<

Updated Salesforce Data-Cloud-Consultant Exam Questions – Key to Your Career Growth

Different from all other bad quality practice materials that cheat you into spending much money on them, our Data-Cloud-Consultant exam materials are the accumulation of professional knowledge worthy practicing and remembering. All intricate points of our Data-Cloud-Consultant Study Guide will not be challenging anymore. They are harbingers of successful outcomes. And our website has already became a famous brand in the market because of our reliable Data-Cloud-Consultant exam questions.

Salesforce Certified Data Cloud Consultant Sample Questions (Q40-Q45):

NEW QUESTION # 40
What does the Ignore Empty Value option do in identity resolution?

A. Ignores Individual object records with empty fields when running identity resolution rules
B. Ignores empty fields when running the standard match rules
C. Ignores empty fields when running any custom match rules
D. Ignores empty fields when running reconciliation rules

Answer: D

Explanation:
The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.
The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.
Data Cloud Identity Resolution Reconciliation Rule Input
Configure Identity Resolution Rulesets
Data and Identity in Data Cloud

NEW QUESTION # 41
Which consideration related to the way Data Cloud ingests CRM data is true?

A. Formula fields are refreshed at regular sync intervals and are updated at the next full refresh.
B. The CRM Connector's synchronization times can be customized to up to 15-minute intervals.
C. CRM data cannot be manually refreshed and must wait for the next scheduled synchronization,
D. The CRM Connector allows standard fields to stream into Data Cloud in real time.

Answer: D

Explanation:
Explanation
The correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time.
This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization. This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.
The other options are incorrect for the following reasons:
* A. CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.
* B. The CRM Connector's synchronization times can be customized to up to 60-minute intervals, not
15-minute intervals3. This option is false.
* C. Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered.
This option is false.
References:
* 1: Connect and Ingest Data in Data Cloud article on Salesforce Help
* 2: Data Sources in Data Cloud unit on Trailhead
* 3: Data Cloud for Admins module on Trailhead
* 4: [Formula Fields in Data Cloud] unit on Trailhead
* : [Data Streams in Data Cloud] unit on Trailhead

NEW QUESTION # 42
A user Is not seeing suggested values from newly-modeled data when building a segment.
What is causing this issue?

A. Value suggestion can only work on direct attributes and not related attributes.
B. Value suggestion requires Data Aware Specialist permissions at a minimum.
C. Value suggestion is still processing and takes up to 24 hours to be available.
D. Value suggestion will only return results for the first 50 values of a specific attribute,

Answer: C

Explanation:
The most likely cause of this issue is that value suggestion is still processing and takes up to 24 hours to be available. Value suggestion is a feature that enables you to see suggested values for data model object (DMO) fields when creating segment filters. However, this feature needs to be enabled for each DMO field, and it can take up to 24 hours for the suggested values to appear after enabling the feature1. Therefore, if a user is not seeing suggested values from newly-modeled data, it could be that the data has not been processed yet by the value suggestion feature. References:
* Use Value Suggestions in Segmentation

NEW QUESTION # 43
What is a reason to create a formula when ingesting a data stream?

A. To transform is date time field into a dale field for use in data mapping
B. To concatenate files so they are ingested in the correct sequence
C. To remove duplicate rows of data from the data stream
D. To add a unique external identifier to an existing ruleset

Answer: A

Explanation:
Creating a formula during data stream ingestion is often done to manipulate or transform data fields to meet specific requirements. In this case, the most common reason is to transform a date-time field into a date field for use in data mapping . Here's why:
Understanding the Requirement
When ingesting data into Salesforce Data Cloud, certain fields may need to be transformed to align with the target data model.
For example, a date-time field (e.g., "2023-10-05T14:30:00Z") may need to be converted into a date field (e.
g., "2023-10-05") for proper mapping and analysis.
Why Transform a Date-Time Field into a Date Field?
Data Mapping Compatibility :
Some data models or downstream systems may only accept date fields (without the time component).
Transforming the field ensures compatibility and avoids errors during ingestion or activation.
Simplified Analysis :
Removing the time component simplifies analysis and reporting, especially when working with daily trends or aggregations.
Standardization :
Converting date-time fields into consistent date formats ensures uniformity across datasets.
Steps to Implement This Solution
Step 1: Identify the Date-Time Field
During the data stream setup, identify the field that contains the date-time value (e.g., "Order_Date_Time").
Step 2: Create a Formula Field
Use the Formula Field option in the data stream configuration to create a new field.
Apply a transformation function (e.g., DATE() or equivalent) to extract the date portion from the date-time field.
Step 3: Map the Transformed Field
Map the newly created date field to the corresponding field in the target data model (e.g., Unified Profile or Data Lake Object).
Step 4: Validate the Transformation
Test the data stream to ensure the transformation works correctly and the date field is properly ingested.
Why Not Other Options?
A). To concatenate files so they are ingested in the correct sequence :Concatenation is not a typical use case for formulas during ingestion. File sequencing is usually handled at the file ingestion level, not through formulas.
B). To add a unique external identifier to an existing ruleset :Adding a unique identifier is typically done during data preparation or identity resolution, not through formulas during ingestion.
D). To remove duplicate rows of data from the data stream :Removing duplicates is better handled through deduplication rules or transformations, not formulas.
Conclusion
The primary reason to create a formula when ingesting a data stream is to transform a date-time field into a date field for use in data mapping . This ensures compatibility, simplifies analysis, and standardizes the data for downstream use.

NEW QUESTION # 44
A consultant needs to create a data graph based on several DLOs,
Which step should the consultant take to make this work?

A. Use a data action to update the data graph with the DLO data
B. Map the DLOs directly to a data graph.
C. Map the DLOS to DMOS and use these in the data graph.
D. Batch transform the DLOs to multiple DMOs and activate these with the data graph.

Answer: C

Explanation:
To create a data graph based on several Data Lake Objects (DLOs) , the consultant should map the DLOs to Data Model Objects (DMOs) and use these in the data graph. Here's why:
Understanding Data Graphs
A data graph in Salesforce Data Cloud represents relationships between entities (e.g., customers, accounts, orders) and their attributes.
It is built using Data Model Objects (DMOs) , which provide a standardized structure for unified profiles and related data.
Why Map DLOs to DMOs?
Role of DLOs and DMOs :
DLOs are raw data sources ingested into Data Cloud.
DMOs are standardized objects used for identity resolution and unified profiles.
Mapping DLOs to DMOs ensures that raw data is transformed into a structured format suitable for data graphs.
Building the Data Graph :
Once the DLOs are mapped to DMOs, the consultant can use the DMOs to define relationships and build the data graph.
This approach ensures consistency and alignment with the unified data model.
Other Options Are Less Suitable :
A . Use a data action to update the data graph with the DLO data : Data actions are used for triggering workflows, not for building data graphs.
C . Map the DLOs directly to a data graph : DLOs cannot be directly mapped to a data graph; they must first be transformed into DMOs.
D . Batch transform the DLOs to multiple DMOs and activate these with the data graph : This is overly complex and unnecessary when mapping DLOs to DMOs suffices.
Steps to Create the Data Graph
Step 1: Map DLOs to DMOs
Navigate to Data Cloud > Data Streams and map the relevant fields from the DLOs to the corresponding DMOs.
Step 2: Define Relationships
Use the Data Model tab to define relationships between DMOs (e.g., linking Individuals to Accounts).
Step 3: Build the Data Graph
Use the mapped DMOs to create the data graph, defining nodes (entities) and edges (relationships).
Step 4: Validate the Graph
Test the data graph to ensure it accurately represents the desired relationships and data flow.
Conclusion
The consultant should map the DLOs to DMOs and use these in the data graph to ensure a structured and consistent approach to building relationships between entities.

NEW QUESTION # 45
......

Clients always wish that they can get immediate use after they buy our Data-Cloud-Consultant Test Questions because their time to get prepared for the exam is limited. Our Data-Cloud-Consultant test torrent won’t let the client wait for too much time and the client will receive the mails in 5-10 minutes sent by our system. Then the client can log in and use our software to learn immediately. It saves the client’s time.

New Data-Cloud-Consultant Exam Name: https://www.prepawayete.com/Salesforce/Data-Cloud-Consultant-practice-exam-dumps.html

DOWNLOAD the newest PrepAwayETE Data-Cloud-Consultant PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1SxJQyVIRyXcWvOT9Wkv0uA348qJGmMyO

Blog

Rob King Rob King

Biography