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

Salesforce DATA-CLOUD-CONSULTANT Online Questions & Answers

• Question 51:

  A consultant is preparing to implement Data Cloud. Which ethic should the consultant adhere to regarding customer data?

  A. Allow senior leaders in the firm to access customer data for audit purposes.
  B. Collect and use all of the data to create more personalized experiences.
  C. Map sensitive data to the same DMO for ease of deletion.
  D. Carefully consider asking for sensitive data such as age, gender, or ethnicity.
  D. Carefully consider asking for sensitive data such as age, gender, or ethnicity.

• Question 52:

  Which two dependencies need to be removed prior to disconnecting a data source? Choose 2 answers

  A. Activation target
  B. Segment
  C. Activation
  D. Data stream
  B. Segment
  D. Data stream

• Question 53:

  An automotive dealership wants to implement Data Cloud. What is a use case for Data Cloud's capabilities?

  A. Implement a full archive solution with version management.
  B. Use browser cookies to track visitor activity on the website and display personalized recommendations.
  C. Build a source of truth for consent management across all unified individuals.
  D. Ingest customer interaction across different touch points, harmonize, and build a data model for analytical reporting.
  D. Ingest customer interaction across different touch points, harmonize, and build a data model for analytical reporting.

• Question 54:

  An organization wants to enable users with the ability to identify and select text attributes from a picklist of options.

  Which Data Cloud feature should help with this use case?

  A. Value suggestion
  B. Data harmonization
  C. Transformation formulas
  D. Global picklists
  A. Value suggestion

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  Value suggestion is a Data Cloud feature that allows users to see and select the possible values for a text field when creating segment filters. Value suggestion can be enabled or disabled for each data model object (DMO) field in the DMO record home. Value suggestion can help users to identify and select text attributes from a picklist of options, without having to type or remember the exact values. Value suggestion can also reduce errors and improve data quality by ensuring consistent and valid values for the segment filters. References: Use Value Suggestions in Segmentation, Considerations for Selecting Related Attributes

• Question 55:

  How does Data Cloud ensure high availability and fault tolerance for customer data?

  A. By distributing data across multiple regions and data centers
  B. By using a data center with robust backups
  C. By Implementing automatic data recovery procedures
  D. By limiting data access to essential personnel
  A. By distributing data across multiple regions and data centers

• Question 56:

  Cumulus Financial uses Service Cloud as its CRM and stores mobile phone, home phone, and work phone as three separate fields for its customers on the Contact record. The company plans to use Data Cloud and ingest the Contact object via the CRM Connector.

  What is the most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation?

  A. Ingest the Contact object and map the Work Phone, Mobile Phone, and Home Phone to the Contact Point Phone data map object from the Contact data stream.
  B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object.
  C. Ingest the Contact object and then create a calculated insight to normalize the phone numbers, and then map to the Contact Point Phone data map object.
  D. Ingest the Contact object and create formula fields in the Contact data stream on the phone numbers, and then map to the Contact Point Phone data map object.
  B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object.

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  The most efficient approach that a consultant should take when ingesting this data to ensure all the different phone numbers are properly mapped and available for use in activation is B. Ingest the Contact object and use streaming transforms to normalize the phone numbers from the Contact data stream into a separate Phone data lake object (DLO) that contains three rows, and then map this new DLO to the Contact Point Phone data map object. This approach allows the consultant to use the streaming transforms feature of Data Cloud, which enables data manipulation and transformation at the time of ingestion, without requiring any additional processing or storage. Streaming transforms can be used to normalize the phone numbers from the Contact data stream, such as removing spaces, dashes, or parentheses, and adding country codes if needed. The normalized phone numbers can then be stored in a separate Phone DLO, which can have one row for each phone number type (work, home, mobile). The Phone DLO can then be mapped to the Contact Point Phone data map object, which is a standard object that represents a phone number associated with a contact point. This way, the consultant can ensure that all the phone numbers are available for activation, such as sending SMS messages or making calls to the customers. The other options are not as efficient as option B. Option A is incorrect because it does not normalize the phone numbers, which may cause issues with activation or identity resolution. Option C is incorrect because it requires creating a calculated insight, which is an additional step that consumes more resources and time than streaming transforms. Option D is incorrect because it requires creating formula fields in the Contact data stream, which may not be supported by the CRM Connector or may cause conflicts with the existing fields in the Contact object. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Streaming Transforms, Contact Point Phone

• Question 57:

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

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  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 systems. The other options are not the best features to highlight for this customer need because: 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 sources. 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 sources. 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 sources. 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]

• Question 58:

  A customer has outlined requirements to trigger a journey for an abandoned browse behavior. Based on the requirements, the consultant determines they will use streaming insights to trigger a data action to Journey Builder every hour.

  How should the consultant configure the solution to ensure the data action is triggered at the cadence required?

  A. Set the activation schedule to hourly.
  B. Configure the data to be ingested in hourly batches.
  C. Set the journey entry schedule to run every hour.
  D. Set the insights aggregation time window to 1 hour.
  D. Set the insights aggregation time window to 1 hour.

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  Streaming insights are computed from real-time engagement events and can be used to trigger data actions based on pre-set rules. Data actions are workflows that send data from Data Cloud to other systems, such as Journey Builder. To ensure that the data action is triggered every hour, the consultant should set the insights aggregation time window to 1 hour. This means that the streaming insight will evaluate the events that occurred within the last hour and execute the data action if the conditions are met. The other options are not relevant for streaming insights and data actions. References: Streaming Insights and Data Actions Limits and Behaviors, Streaming Insights, Streaming Insights and Data Actions Use Cases, Use Insights in Data Cloud, 6 Ways the Latest Marketing Cloud Release Can Boost Your Campaigns

• Question 59:

  A retail customer wants to bring customer data from different sources and wants to take advantage of identity resolution so that it can be used in segmentation.

  On which entity should this be segmented for activation membership?

  A. Subscriber
  B. Unified Individual
  C. Unified Contact
  D. Individual
  B. Unified Individual

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  The correct answer is B, Unified Individual. A Unified Individual is a record that represents a customer across different data sources, created by applying identity resolution rulesets. Identity resolution rulesets are sets of match and reconciliation rules that define how to link and merge data from different sources based on common attributes. Data Cloud uses identity resolution rulesets to resolve data across multiple data sources and helps you create one record for each customer, regardless of where the data came from1. A retail customer who wants to bring customer data from different sources and use identity resolution for segmentation should segment on the Unified Individual entity, which contains the resolved and consolidated customer data. The other options are incorrect because they do not represent the resolved customer data across different sources. A Subscriber is a record that represents a customer who has opted in to receive marketing communications. A Unified Contact is a record that represents a customer who has a relationship with a specific business unit. An Individual is a record that represents a customer's profile data from a single data source. References: 1. Identity Resolution Ruleset Processing Results 2. Consider Data Implications for Segmentation 3. Prepare for your Salesforce Data Cloud Consultant Credential 4. AI-based Identity Resolution: Linking Diverse Customer Data

• Question 60:

  Northern Trail Outfitters (NTO) owns and operates six unique brands, each with their own set of customers, transactions, and loyalty information. The marketing director wants to ensure that segments and activations from the NTO Outlet brand do not reference customers or transactions from the other brands.

  What is the most efficient approach to handle this requirement?

  A. Use Business Unit Aware activation.
  B. Separate the Outlet brand into a data space.
  C. Separate the brands into six different data spaces.
  D. Create a batch data transform to generate a DLO for the Outlet brand.
  B. Separate the Outlet brand into a data space.