Salesforce Salesforce Certifications DATA-CLOUD-CONSULTANT Questions & Answers

• Question 91:

  A consultant is planning the ingestion of a data stream that has profile information including a mobile phone number.

  To ensure that the phone number can be used for future SMS campaigns, they need to confirm the phone number field is in the proper E164 Phone Number format. However, the phone numbers in the file appear to be in varying formats.

  What is the most efficient way to guarantee that the various phone number formats are standardized?

  A. Create a formula field to standardize the format.

  B. Edit and update the data in the source system prior to sending to Data Cloud.

  C. Assign the PhoneNumber field type when creating the data stream.

  D. Create a calculated insight after ingestion.

  Correct Answer: C

  The most efficient way to guarantee that the various phone number formats are standardized is to assign the PhoneNumber field type when creating the data stream. The PhoneNumber field type is a special field type that automatically converts phone numbers into the E164 format, which is the international standard for phone numbers. The E164 format consists of a plus sign (+), the country code, and the national number. For example, +1-202-555-1234 is the E164 format for a US phone number. By using the PhoneNumber field type, the consultant can ensure that the phone numbers are consistent and can be used for future SMS campaigns. The other options are either more time-consuming, require manual intervention, or do not address the formatting issue. References: Data Stream Field Types, E164 Phone Number Format, Salesforce Data Cloud Exam Questions

• Question 92:

  A user wants to be able to create a multi-dimensional metric to identify unified individual lifetime value (LTV).

  Which sequence of data model object (DMO) joins is necessary within the calculated Insight to enable this calculation?

  A. Unified Individual > Unified Link Individual > Sales Order

  B. Unified Individual > Individual > Sales Order

  C. Sales Order > Individual > Unified Individual

  D. Sales Order > Unified Individual

  Correct Answer: A

  To create a multi-dimensional metric to identify unified individual lifetime value (LTV), the sequence of data model object (DMO) joins that is necessary within the calculated Insight is Unified Individual > Unified Link Individual > Sales Order. This is because the Unified Individual DMO represents the unified profile of an individual or entity that is created by identity resolution1. The Unified Link Individual DMO represents the link between a unified individual and an individual from a source system2. The Sales Order DMO represents the sales order information from a source system3. By joining these three DMOs, you can calculate the LTV of a unified individual based on the sales order data from different source systems. The other options are incorrect because they do not join the correct DMOs to enable the LTV calculation. Option B is incorrect because the Individual DMO represents the source profile of an individual or entity from a source system, not the unified profile4. Option C is incorrect because the join order is reversed, and you need to start with the Unified Individual DMO to identify the unified profile. Option D is incorrect because it is missing the Unified Link Individual DMO, which is needed to link the unified profile with the source profile. References: Unified Individual Data Model Object, Unified Link Individual Data Model Object, Sales Order Data Model Object, Individual Data Model Object

• Question 93:

  A Data Cloud customer wants to adjust their identity resolution rules to increase their accuracy of matches. Rather than matching on email address, they want to review a rule that joins their CRM Contacts with their Marketing Contacts, where both use the CRM ID as their primary key.

  Which two steps should the consultant take to address this new use case? Choose 2 answers

  A. Map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both.

  B. Map the primary key from the two systems to party identification, using CRM ID as the identification name for individuals coming from the CRM, and Marketing ID as the identification name for individuals coming from the marketing platform.

  C. Create a custom matching rule for an exact match on the Individual ID attribute.

  D. Create a matching rule based on party identification that matches on CRM ID as the party identification name.

  Correct Answer: AD

  To address this new use case, the consultant should map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both, and create a matching rule based on party identification that matches on CRM ID as the party identification name. This way, the consultant can ensure that the CRM Contacts and Marketing Contacts are matched based on their CRM ID, which is a unique identifier for each individual. By using Party Identification, the consultant can also leverage the benefits of this attribute, such as being able to match across different entities and sources, and being able to handle multiple values for the same individual. The other options are incorrect because they either do not use the CRM ID as the primary key, or they do not use Party Identification as the attribute type. References: Configure Identity Resolution Rulesets, Identity Resolution Match Rules, Data Cloud Identity Resolution Ruleset, Data Cloud Identity Resolution Config Input

• Question 94:

  What should an organization use to stream inventory levels from an inventory management system into Data Cloud in a fast and scalable, near-real-time way?

  A. Cloud Storage Connector

  B. Commerce Cloud Connector

  C. Ingestion API

  D. Marketing Cloud Personalization Connector

  Correct Answer: C

  The Ingestion API is a RESTful API that allows you to stream data from any source into Data Cloud in a fast and scalable way. You can use the Ingestion API to send data from your inventory management system into Data Cloud as JSON objects, and then use Data Cloud to create data models, segments, and insights based on your inventory data. The Ingestion API supports both batch and streaming modes, and can handle up to 100,000 records per second. The Ingestion API also provides features such as data validation, encryption, compression, and retry mechanisms to ensure data quality and security. References: Ingestion API Developer Guide, Ingest Data into Data Cloud

• Question 95:

  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.

  Correct Answer: B

  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 96:

  A consultant is helping a beauty company ingest its profile data into Data Cloud. The company's source data includes several fields, such as eye color, skin type, and hair color, that are not fields in the standard Individual data model object (DMO).

  What should the consultant recommend to map this data to be used for both segmentation and identity resolution?

  A. Create a custom DMO from scratch that has all fields that are needed.

  B. Create a custom DMO with only the additional fields and map it to the standard Individual DMO.

  C. Create custom fields on the standard Individual DMO.

  D. Duplicate the standard Individual DMO and add the additional fields.

  Correct Answer: C

  The best option to map the data to be used for both segmentation and identity resolution is to create custom fields on the standard Individual DMO. This way, the consultant can leverage the existing fields and functionality of the Individual DMO, such as identity resolution rulesets, calculated insights, and data actions, while adding the additional fields that are specific to the beauty company's data1. Creating a custom DMO from scratch or duplicating the standard Individual DMO would require more effort and maintenance, and might not be compatible with the existing features of Data Cloud. Creating a custom DMO with only the additional fields and mapping it to the standard Individual DMO would create unnecessary complexity and redundancy, and might not allow the use of the custom fields for identity resolution.

• Question 97:

  Which two steps should a consultant take if a successfully configured Amazon S3 data stream fails to refresh with a "NO FILE FOUND" error message?

  Choose 2 answers

  A. Check if correct permissions are configured for the Data Cloud user.

  B. Check if the Amazon S3 data source is enabled in Data Cloud Setup.

  C. Check If the file exists in the specified bucket location.

  D. Check if correct permissions are configured for the S3 user.

  Correct Answer: AC

  A "NO FILE FOUND" error message indicates that Data Cloud cannot access or locate the file from the Amazon S3 source. There are two possible reasons for this error and two corresponding steps that a consultant should take to troubleshoot it:

  The Data Cloud user does not have the correct permissions to read the file from the Amazon S3 bucket. This could happen if the user's permission set or profile does not include the Data Cloud Data Stream Read permission, or if the user's Amazon S3 credentials are invalid or expired. To fix this issue, the consultant should check and update the user's permissions and credentials in Data Cloud and Amazon S3, respectively. The file does not exist in the specified bucket location. This could happen if the file name or path has changed, or if the file has been deleted or moved from the Amazon S3 bucket. To fix this issue, the consultant should check and verify the file name and path in the Amazon S3 bucket, and update the data stream configuration in Data Cloud accordingly. References: Create Amazon S3 Data Stream in Data Cloud, How to Use the Amazon S3 Storage Connector in Data Cloud, Amazon S3 Connection

• Question 98:

  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, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.

  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. 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.

  D. 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.

  Correct Answer: C

  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.

• Question 99:

  A consultant needs to package Data Cloud components from one organization to another.

  Which two Data Cloud components should the consultant include in a data kit to achieve this goal?

  Choose 2 answers

  A. Data model objects

  B. Segments

  C. Calculated insights

  D. Identity resolution rulesets

  Correct Answer: AD

  To package Data Cloud components from one organization to another, the consultant should include the following components in a data kit:

  Data model objects: These are the custom objects that define the data model for Data Cloud, such as Individual, Segment, Activity, etc. They store the data ingested from various sources and enable the creation of unified profiles and

  segments.

  Identity resolution rulesets: These are the rules that determine how data from different sources are matched and merged to create unified profiles. They specify the criteria, logic, and priority for identity resolution2. References:

  1: Data Model Objects in Data Cloud

  2: Identity Resolution Rulesets in Data Cloud

• Question 100:

  The recruiting team at Cumulus Financial wants to identify which candidates have browsed the jobs page on its website at least twice within the last 24 hours. They want the information about these candidates to be available for segmentation in Data Cloud and the candidates added to their recruiting system.

  Which feature should a consultant recommend to achieve this goal?

  A. Streaming data transform

  B. Streaming insight

  C. Calculated insight

  D. Batch bata transform

  Correct Answer: B

  A streaming insight is a feature that allows users to create and monitor real-time metrics from streaming data sources, such as web and mobile events. A streaming insight can also trigger data actions, such as sending notifications, creating records, or updating fields, based on the metric values and conditions. Therefore, a streaming insight is the best feature to achieve the goal of identifying candidates who have browsed the jobs page on the website at least twice within the last 24 hours, and adding them to the recruiting system. The other options are incorrect because:

  A streaming data transform is a feature that allows users to transform and enrich streaming data using SQL expressions, such as filtering, joining, aggregating, or calculating values. However, a streaming data transform does not provide the

  ability to monitor metrics or trigger data actions based on conditions.

  A calculated insight is a feature that allows users to define and calculate multidimensional metrics from data using SQL expressions, such as LTV, CSAT, or average order value. However, a calculated insight is not suitable for real-time data

  analysis, as it runs on a scheduled basis and does not support data actions.

  A batch data transform is a feature that allows users to create and schedule complex data transformations using a visual editor, such as joining, aggregating, filtering, or appending data. However, a batch data transform is not suitable for real-

  time data analysis, as it runs on a scheduled basis and does not support data actions. References: Streaming Insights, Create a Streaming Insight, Use Insights in Data Cloud, Learn About Data Cloud Insights, Data Cloud Insights Using SQL,

  Streaming Data Transforms, Get Started with Batch Data Transforms in Data Cloud, Transformations for Batch Data Transforms, Batch Data Transforms in Data Cloud: Quick Look, Salesforce Data Cloud: AI CDP.