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Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 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.
Topic 5
  • 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.

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Salesforce Certified Data 360 Consultant (Data-Con-101) Sample Questions (Q80-Q85):

NEW QUESTION # 80
What is the result of a segmentation criteria filtering on City | Is Equal To | 'San Jose'?

Answer: C

Explanation:
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 # 81
How can a consultant modify attribute names to match a naming convention in Cloud File Storage targets?

Answer: A


NEW QUESTION # 82
Which method should a consultant use when performing aggregations in windows of 15 minutes on data collected via the Interaction SDK or Mobile SDK?

Answer: D

Explanation:
Streaming insight is a method that allows you to perform aggregations in windows of 15 minutes on data collected via the Interaction SDK or Mobile SDK. Streaming insight is a feature that enables you to create real-time metrics and insights based on streaming data from various sources, such as web, mobile, or IoT devices. Streaming insight allows you to define aggregation rules, such as count, sum, average, min, max, or percentile, and apply them to streaming data in time windows of 15 minutes. For example, you can use streaming insight to calculate the number of visitors, the average session duration, or the conversion rate for your website or app in 15-minute intervals. Streaming insight also allows you to visualize and explore the aggregated data in dashboards, charts, or tables. Reference: Streaming Insight, Create Streaming Insights


NEW QUESTION # 83
Northern Trail Outfitters (NTO) wants to connect their B2C Commerce data with Data Cloud and bring two years of transactional history into Data Cloud.
What should NTO use to achieve this?

Answer: B

Explanation:
The B2C Commerce Starter Bundles are predefined data streams that ingest order and product data from B2C Commerce into Data Cloud. However, the starter bundles only bring in the last 90 days of data by default. To bring in two years of transactional history, NTO needs to use a custom extract from B2C Commerce that includes the historical data and configure the data stream to use the custom extract as the source. The other options are not sufficient to achieve this because:
* A. B2C Commerce Starter Bundles only ingest the last 90 days of data by default.
* B. Direct Sales Order entity ingestion is not a supported method for connecting B2C Commerce data
* with Data Cloud. Data Cloud does not provide a direct-access connection for B2C Commerce data, only data ingestion.
* C. Direct Sales Product entity ingestion is not a supported method for connecting B2C Commerce data with Data Cloud. Data Cloud does not provide a direct-access connection for B2C Commerce data, only data ingestion. References: Create a B2C Commerce Data Bundle - Salesforce, B2C Commerce Connector - Salesforce, Salesforce B2C Commerce Pricing Plans & Costs


NEW QUESTION # 84
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?

Answer: C

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 # 85
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