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Data-Cloud-Consultant퍼펙트덤프데모다운로드인증시험기출자료
Salesforce인증 Data-Cloud-Consultant시험을 패스하여 자격증을 취득하여 승진이나 이직을 꿈구고 있는 분이신가요? 이 글을 읽게 된다면Salesforce인증 Data-Cloud-Consultant시험패스를 위해 공부자료를 마련하고 싶은 마음이 크다는것을 알고 있어 시장에서 가장 저렴하고 가장 최신버전의 Salesforce인증 Data-Cloud-Consultant덤프자료를 강추해드립니다. 높은 시험패스율을 자랑하고 있는Salesforce인증 Data-Cloud-Consultant덤프는 여러분이 승진으로 향해 달리는 길에 날개를 펼쳐드립니다.자격증을 하루 빨리 취득하여 승진꿈을 이루세요.
Salesforce Data-Cloud-Consultant 시험요강:
| 주제 |
소개 |
| 주제 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.
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| 주제 2 |
- 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.
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| 주제 3 |
- 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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| 주제 4 |
- 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.
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>> Data-Cloud-Consultant퍼펙트 덤프데모 다운로드 <<
Data-Cloud-Consultant 100％시험패스 덤프 - Data-Cloud-Consultant시험패스
Salesforce인증 Data-Cloud-Consultant시험을 어떻게 공부하면 패스할수 있을지 고민중이시면 근심걱정 버리시고Fast2test 의 Salesforce인증 Data-Cloud-Consultant덤프로 가보세요. 문항수가 적고 적중율이 높은 세련된Salesforce인증 Data-Cloud-Consultant시험준비 공부자료는Fast2test제품이 최고입니다.
최신 Salesforce Data Cloud Data-Cloud-Consultant 무료샘플문제 (Q33-Q38):
질문 # 33
Which two requirements must be met for a calculated insight to appear in the segmentation canvas?
Choose 2 answers
- A. The calculated insight must contain a dimension including the Individual or Unified Individual Id.
- B. The primary key of the segmented table must be a dimension in the calculated insight.
- C. The primary key of the segmented table must be a metric in the calculated insight.
- D. The metrics of the calculated insights must only contain numeric values.
정답：A,B
설명：
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.
References: Create a Calculated Insight, Use Insights in Data Cloud, Segmentation
질문 # 34
Which consideration related to the way Data Cloud ingests CRM data is true?
- A. The CRM Connector's synchronization times can be customized to up to 15-minute intervals.
- B. Formula fields are refreshed at regular sync intervals and are updated at the next full refresh.
- 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.
정답：D
설명：
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
질문 # 35
A consultant at Northern Trail Outfitters is attempting to ingest a field from the Contact object in Salesforce CRM that contains both yyyy-mm-dd and yyyy-mm-dd hh:mm:ss values. The target field is set to Date datatype.
Which statement is true in this situation?
- A. The target field will only hold the time part and ignore the date part.
- B. The target field will throw an error and store null values.
- C. The target field will be able to hold both types of values.
- D. The target field will only hold the date part and ignore the time part.
정답：D
설명：
* Field Data Types: Salesforce CRM's Contact object fields can store data in various formats. When ingesting data into Salesforce Data Cloud, the target field's data type determines how the data is processed and stored.
* Date Data Type: If the target field in Data Cloud is set to Date data type, it is designed to store date values without time information.
* Mixed Format Values: When ingesting a field containing both date (yyyy-mm-dd) and datetime (yyyy-mm-dd hh:mm:ss) values into a Date data type field:
The Date field will extract and store only the date part (yyyy-mm-dd), ignoring the time part (hh:mm:ss).
* Result:
Date Values: yyyy-mm-dd values are stored as-is.
Datetime Values: yyyy-mm-dd hh:mm:ss values are truncated to yyyy-mm-dd, and the time component is ignored.
* Reference:
Salesforce Data Cloud Field Mapping
Salesforce Data Types
질문 # 36
Northern Trail Outfitters (NTO), an outdoor lifestyle clothing brand, recently started a new line of business. The new business specializes in gourmet camping food. For business reasons as well as security reasons, it's important to NTO to keep all Data Cloud data separated by brand.
Which capability best supports NTO's desire to separate its data by brand?
- A. Data streams for each brand
- B. Data spaces for each brand
- C. Data sources for each brand
- D. Data model objects for each brand
정답：B
설명：
Explanation
Data spaces are logical containers that allow you to separate and organize your data by different criteria, such as brand, region, product, or business unit1. Data spaces can help you manage data access, security, and governance, as well as enable cross-cloud data integration and activation2. For NTO, data spaces can support their desire to separate their data by brand, so that they can have different data models, rules, and insights for their outdoor lifestyle clothing and gourmet camping food businesses. Data spaces can also help NTO comply with any data privacy and security regulations that may apply to their different brands3. The other options are incorrect because they do not provide the same level of data separation and organization as data spaces. Data streams are used to ingest data from different sources into Data Cloud, but they do not separate the data by brand4. Data model objects are used to definethe structure and attributes of the data, but they do not isolate the data by brand5. Data sources are used to identify the origin and type of the data, but they do not partition the data by brand. References: Data Spaces Overview, Create Data Spaces, Data Privacy and Security in Data Cloud, Data Streams Overview, Data Model Objects Overview, [Data Sources Overview]
질문 # 37
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, compare the two rulesets to review and verify the results, and then migrate to the new ruleset once approved.
- 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, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.
- 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.
정답：D
설명：
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.
질문 # 38
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