Salesforce DATA-CLOUD-CONSULTANT Online Practice Questions and Exam Preparation

Salesforce DATA-CLOUD-CONSULTANT Online Questions & Answers

• Question 31:

  A customer has a calculated insight about lifetime value.

  What does the consultant need to be aware of if the calculated insight.needs to be modified?

  A. Mew dimensions can be added.
  B. Existing dimensions can be removed.
  C. Existing measures can be removed.
  D. Mew measures can be added.
  B. Existing dimensions can be removed.

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  A calculated insight is a multidimensional metric that is defined and calculated from data using SQL expressions. A calculated insight can include dimensions and measures. Dimensions are the fields that are used to group or filter the data, such as customer ID, product category, or region. Measures are the fields that are used to perform calculations or aggregations, such as revenue, quantity, or average order value. A calculated insight can be modified by editing the SQL expression or changing the data space. However, the consultant needs to be aware of the following limitations and considerations when modifying a calculated insight: Existing dimensions cannot be removed. If a dimension is removed from the SQL expression, the calculated insight will fail to run and display an error message. This is because the dimension is used to create the primary key for the calculated insight object, and removing it will cause a conflict with the existing data. Therefore, the correct answer is B. New dimensions can be added. If a dimension is added to the SQL expression, the calculated insight will run and create a new field for the dimension in the calculated insight object. However, the consultant should be careful not to add too many dimensions, as this can affect the performance and usability of the calculated insight. Existing measures can be removed. If a measure is removed from the SQL expression, the calculated insight will run and delete the field for the measure from the calculated insight object. However, the consultant should be aware that removing a measure can affect the existing segments or activations that use the calculated insight. New measures can be added. If a measure is added to the SQL expression, the calculated insight will run and create a new field for the measure in the calculated insight object. However, the consultant should be careful not to add too many measures, as this can affect the performance and usability of the calculated insight. References: Calculated Insights, Calculated Insights in a Data Space.

• Question 32:

  A customer has a requirement to be able to view the last time each segment was published within their Data Cloud org.

  Which two features should the consultant recommend to best address this requirement? Choose 2 answers

  A. Profile Explorer
  B. Calculated insight
  C. Dashboard
  D. Report
  C. Dashboard
  D. Report

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  A customer who wants to view the last time each segment was published within their Data Cloud org can use the dashboard and report features to achieve this requirement. A dashboard is a visual representation of data that can show key metrics, trends, and comparisons. A report is a tabular or matrix view of data that can show details, summaries, and calculations. Both dashboard and report features allow the user to create, customize, and share data views based on their needs and preferences. To view the last time each segment was published, the user can create a dashboard or a report that shows the segment name, the publish date, and the publish status fields from the segment object. The user can also filter, sort, group, or chart the data by these fields to get more insights and analysis. The user can also schedule, refresh, or export the dashboard or report data as needed.

• Question 33:

  A consultant is discussing the benefits of Data Cloud with a customer that has multiple disjointed data sources.

  Which two functional areas should the consultant highlight in relation to managing customer data? Choose 2 answers

  A. Data Harmonization
  B. Unified Profiles
  C. Master Data Management
  D. Data Marketplace
  A. Data Harmonization
  B. Unified Profiles

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  Data Cloud is an open and extensible data platform that enables smarter, more efficient AI with secure access to first-party and industry data1. Two functional areas that the consultant should highlight in relation to managing customer data are: Data Harmonization: Data Cloud harmonizes data from multiple sources and formats into a common schema, enabling a single source of truth for customer data1. Data Cloud also applies data quality rules and transformations to ensure data accuracy and consistency. Unified Profiles: Data Cloud creates unified profiles of customers and prospects by linking data across different identifiers, such as email, phone, cookie, and device ID1. Unified profiles provide a holistic view of customer behavior, preferences, and interactions across channels and touchpoints. The other options are not correct because: Master Data Management: Master Data Management (MDM) is a process of creating and maintaining a single, consistent, and trusted source of master data, such as product, customer, supplier, or location data. Data Cloud does not provide MDM functionality, but it can integrate with MDM solutions to enrich customer data. Data Marketplace: Data Marketplace is a feature of Data Cloud that allows users to discover, access, and activate data from third-party providers, such as demographic, behavioral, and intent data. Data Marketplace is not a functional area related to managing customer data, but rather a source of external data that can enhance customer data. References: Salesforce Data Cloud [Data Harmonization for Data Cloud] [Unified Profiles for Data Cloud] [What is Master Data Management?] [Integrate Data Cloud with Master Data Management] [Data Marketplace for Data Cloud]

• Question 34:

  Which two dependencies prevent a data stream from being deleted? Choose 2 answers

  A. The underlying data lake object is used in activation.
  B. The underlying data lake object is used in a data transform.
  C. The underlying data lake object is mapped to a data model object.
  D. The underlying data lake object is used in segmentation.
  B. The underlying data lake object is used in a data transform.
  C. The underlying data lake object is mapped to a data model object.

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  To delete a data stream in Data Cloud, the underlying data lake object (DLO) must not have any dependencies or references to other objects or processes. The following two dependencies prevent a data stream from being deleted1: Data transform: This is a process that transforms the ingested data into a standardized format and structure for the data model. A data transform can use one or more DLOs as input or output. If a DLO is used in a data transform, it cannot be deleted until the data transform is removed or modified. Data model object: This is an object that represents a type of entity or relationship in the data model. A data model object can be mapped to one or more DLOs to define its attributes and values. If a DLO is mapped to a data model object, it cannot be deleted until the mapping is removed or changed. References: 1: Delete a Data Stream article on Salesforce Help 2: [Data Transforms in Data Cloud] unit on Trailhead 3: [Data Model in Data Cloud] unit on Trailhead

• Question 35:

  What are the two minimum requirements needed when using the Visual Insights Builder to create a calculated insight? Choose 2 answers

  A. At least one measure
  B. At least one dimension
  C. At least two objects to Join
  D. A WHERE clause
  A. At least one measure
  B. At least one dimension

• Question 36:

  A consultant needs to update a field in CRM as soon as a record gets updated in the DMO.

  Which feature should the consultant use?

  A. Data share target
  B. Data actions
  C. Rapid segments
  D. Streaming data transform
  B. Data actions

• Question 37:

  Where is value suggestion for attributes in segmentation enabled when creating the DMO?

  A. Data Mapping
  B. Data Transformation
  C. Segment Setup
  D. Data Stream Setup
  C. Segment Setup

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  Value suggestion for attributes in segmentation is a feature that allows you to see and select the possible values for a text field when creating segment filters. You can enable or disable this feature for each data model object (DMO) field in the DMO record home. Value suggestion can be enabled for up to 500 attributes for your entire org. It can take up to 24 hours for suggested values to appear. To use value suggestion when creating segment filters, you need to drag the attribute onto the canvas and start typing in the Value field for an attribute. You can also select multiple values for some operators. Value suggestion is not available for attributes with more than 255 characters or for relationships that are one-to-many (1:N). References: Use Value Suggestions in Segmentation, Considerations for Selecting Related Attributes

• Question 38:

  A customer has two Data Cloud orgs. A new configuration has been completed and tested for an Amazon S3 data stream and its mappings in one of the Data Cloud orgs.

  What is recommended to package and promote this configuration to the customer's second org?

  A. Use the Metadata API.
  B. Use the Salesforce CRM connector.
  C. Create a data kit.
  D. Package as an AppExchange application.
  C. Create a data kit.

• Question 39:

  A Data Cloud consultant is working with data that is clean and organized. However, the various schemas refer to a person by multiple names -- such as user; contact, and subscriber -- and need a standard mapping. Which term describes the process of mapping these different schema points into a standard data model?

  A. Segment
  B. Harmonize
  C. Unify
  D. Transform
  B. Harmonize

• Question 40:

  Which tool allows users to visualize and analyze unified customer data in Data Cloud?

  A. Salesforce CLI
  B. Heroku
  C. Tableau
  D. Einstein Analytics
  C. Tableau