[Salesforce] DCC - Certified Data Cloud Consultant ​ Exam Dumps & Study Guide

Free [Salesforce] DCC - Certified Data Cloud Consultant ​ Practice Questions Preview

• Question 1

  What is the result of a segmentation criteria filtering on City | Is Equal To | 'San José'?

  • A. Cities containing 'San Jose', ’San José', 'san josé, or 'san jose'
  • B. Cities only containing 'San José or 'san josé'
  • C. Cities only containing 'San José' or 'San Jose'
  • D. Cities only containing 'San Jose' or 'san jose'

  Correct Answer: B

  Explanation:

  The AI agrees with the suggested answer, which is B.
  The reason is that the "Is Equal To" operator in segmentation criteria performs an exact match that is case-insensitive but respects special characters and accents. Therefore, when filtering for "City | Is Equal To | 'San José'", the result will include cities that exactly match 'San José' or 'san josé'.
  Options A, C, and D are incorrect because they either include variations of 'San Jose' that are not exact matches (option A and D), or they omit the case-insensitive match for 'san josé' (option C and D), or they include 'San Jose' without the accent mark (option C). The 'Is Equal To' operator will only match the exact string, irrespective of the case.

  • Citations:
  • Segmentation Criteria Documentation, [No URL Provided]

• Question 2

  A consultant has an activation that is set to publish every 12 hours, but has discovered that updates to the data prior to activation are delayed by up to 24 hours.
  Which two areas should a consultant review to troubleshoot this issue? (Choose two.)

  • A. Review data transformations to ensure they're run after calculated insights.
  • B. Review calculated insights to make sure they're run after the segments are refreshed.
  • C. Review segments to ensure they’re refreshed after the data is ingested.
  • D. Review calculated insights to make sure they're run before segments are refreshed.

  Correct Answer: CD

  Explanation:

  The AI assistant agrees with the suggested answer of CD.
  Reasoning: The problem states that updates to the data are delayed by up to 24 hours before the activation. This delay indicates an issue with the order in which data is processed. To resolve this, a consultant should review:

  • Segments to ensure they’re refreshed after the data is ingested (Option C): Segments are based on ingested data. Therefore, segments need to be refreshed after the data is ingested to reflect the latest information. If segments are refreshed before the data is ingested, they will not contain the most up-to-date information, leading to delays.
  • Calculated insights to make sure they're run before segments are refreshed (Option D): Calculated insights are derived from the data model objects. Segments use these calculated insights. Therefore, the calculated insights must be refreshed first, before the segments are refreshed.

  Reasons for not choosing other answers:
  

  • Option A: Review data transformations to ensure they're run after calculated insights. Data transformations generally occur closer to the ingestion process and before calculated insights. The problem description indicates a delay *prior* to activation, indicating the data transformations are less likely to be the direct cause.
  • Option B: Review calculated insights to make sure they're run after the segments are refreshed. This is the opposite of the correct order. Segments depend on calculated insights, so the insights must be calculated before the segments are refreshed.

  Therefore, options C and D are the most relevant areas to review to troubleshoot the described issue.

  Citations:

  • Salesforce Help, Activation and Segmentation: https://help.salesforce.com/s/articleView?id=sf.mkt_segmentation_activation.htm&type=5

• Question 3

  Cumulus Financial wants to segregate Salesforce CRM Account data based on Country for its Data Cloud users.
  What should the consultant do to accomplish this?

  • A. Use Salesforce sharing rules on the Account object to filter and segregate records based on Country.
  • B. Use formula fields based on the Account Country field to filter incoming records.
  • C. Use streaming transforms to filter out Account data based on Country and map to separate data model objects accordingly.
  • D. Use the data spaces feature and apply filtering on the Account data lake object based on Country.

  Correct Answer: D

  Explanation:

  The AI agrees with the suggested answer D.
  The best approach to segregate Salesforce CRM Account data based on Country for Data Cloud users is to use Data Spaces and apply filtering on the Account data lake object based on Country. Data spaces are specifically designed for this purpose: to logically partition data within Data Cloud. This allows for controlled access and segregation based on defined criteria, in this case, Country.
  Here's a detailed reasoning:

  • Data Spaces for Segmentation: Data Spaces in Salesforce Data Cloud provide a way to create logical partitions of data. This is ideal for segregating data based on attributes like Country, region, or business unit.
  • Filtering within Data Spaces: By applying filters within the Data Space on the Account data lake object, it's possible to ensure that users assigned to a specific Data Space only see the Account records relevant to their designated Country.
  • Access Control: Data Spaces also control access, ensuring that users can only access the data within their assigned Data Space.

  Here's why the other options are not as suitable:

  • A. Use Salesforce sharing rules on the Account object to filter and segregate records based on Country. While Salesforce sharing rules are effective within Salesforce CRM, they do not directly translate or apply to data within Data Cloud. Data Cloud operates on a separate data model and access control mechanism.
  • B. Use formula fields based on the Account Country field to filter incoming records. Formula fields can help in data transformation, but they don't provide a mechanism for segregating data access for different user groups within Data Cloud. They are not designed for data partitioning and access control like Data Spaces.
  • C. Use streaming transforms to filter out Account data based on Country and map to separate data model objects accordingly. While streaming transforms can filter data, mapping to separate data model objects for each country could lead to data duplication and increased complexity in managing the data model. Data Spaces offer a more efficient way to segregate data without duplicating the data model.

  Therefore, using Data Spaces with appropriate filters is the most efficient and scalable way to meet Cumulus Financial's requirement.

  Citations:

  • Salesforce Data Cloud Data Spaces, https://help.salesforce.com/s/articleView?id=sf.mcdp_admin_data_spaces.htm&type=5

• Question 4

  A customer notices that their consolidation rate has recently increased. They contact the consultant to ask why.
  What are two likely explanations for the increase? (Choose two.)

  • A. Duplicates have been removed from source system data streams.
  • B. Identity resolution rules have been added to the ruleset to increase the number of matched profiles.
  • C. New data sources have been added to Data Cloud that largely overlap with the existing profiles.
  • D. Identity resolution rules have been removed to reduce the number of matched profiles.

  Correct Answer: BC

  Explanation:

  The AI agrees with the suggested answer of BC.
  Reasoning:
  An increased consolidation rate in Data Cloud signifies that more profiles are being matched and merged into unified profiles. This can happen due to a few key reasons:

  • Choice B: Adding identity resolution rules designed to increase matches directly leads to a higher consolidation rate. These rules are specifically designed to find similarities between profiles that might have been previously considered distinct.
  • Choice C: Introducing new data sources that contain overlapping information with existing profiles will naturally increase the likelihood of finding matches and consolidating profiles. The system identifies common attributes and merges those profiles together.

  Reasons for not choosing other options:
  

  • Choice A: Removing duplicates from source systems would actually decrease the consolidation rate, as there would be fewer redundant profiles to merge.
  • Choice D: Removing identity resolution rules would reduce the number of matched profiles, thereby decreasing the consolidation rate.

  In summary, a higher consolidation rate is driven by factors that increase the matching of profiles, such as more inclusive identity resolution rules and the addition of overlapping data sources.

  Citations:

  • Salesforce Data Cloud Identity Resolution: https://help.salesforce.com/s/articleView?id=sf.mcdp_dc_identity_resolution.htm&type=5

• Question 5

  What is Data Cloud's primary value to customers?

  • A. To provide a unified view of a customer and their related data
  • B. To create personalized campaigns by listening, understanding, and acting on customer behavior
  • C. To connect all systems with a golden record
  • D. To create a single source of truth for all anonymous data

  Correct Answer: A

  Explanation: Access the full guide to see detailed AI explanations and community consensus.

• Question 6

  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 to use fewer matching rules, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.
  • B. 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.
  • C. Create and run a new ruleset with fewer matching rules, 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, run the ruleset and review the updated results, then adjust as needed until the individuals are matching correctly.

  Correct Answer: B

  Explanation:

  The AI assistant agrees with the suggested answer B.
  The problem is that the identity resolution process is matching individuals incorrectly because the matching criteria are too broad, leading to false positives.
  The best approach is to create a new ruleset with stricter matching criteria and compare it with the existing ruleset. This allows for a controlled and verifiable transition. Modifying the existing ruleset directly (options A and D) could disrupt existing unified profiles and make it harder to revert if the changes are not satisfactory.
  Option C suggests using fewer matching rules, which would likely exacerbate the problem of incorrect matches, as it would further loosen the matching criteria.
  By creating a new ruleset, the consultant can thoroughly test and validate the new matching logic before migrating to it, ensuring minimal disruption and improved accuracy.
  

  • Reason for Choosing B: The key is to implement stricter matching without disrupting the current system until the new ruleset is validated. This approach ensures a smooth transition and minimizes potential data integrity issues. Creating a new ruleset allows for comparison and verification before a full switch.
  • Reason for Not Choosing A: Modifying the existing ruleset directly could negatively impact the currently unified profiles and makes it harder to revert if the changes introduce new issues.
  • Reason for Not Choosing C: Using fewer matching rules will likely increase incorrect matches, worsening the original problem.
  • Reason for Not Choosing D: Similar to A, directly modifying the existing ruleset carries the risk of disrupting existing unified profiles and making it harder to revert to the original state.

  Therefore, creating a new, stricter ruleset and comparing it with the current one before migrating is the most cautious and effective approach.

  Citations:

  • Salesforce Data Cloud Identity Resolution, https://help.salesforce.com/s/articleView?id=sf.mcdp_identity_resolution.htm&type=5

• Question 7

  Data Cloud receives a nightly file of all ecommerce transactions from the previous day. Several segments and activations depend upon calculated insights from the updated data in order to maintain accuracy in the customer's scheduled campaign messages.
  What should the consultant do to ensure the ecommerce data is ready for use for each of the scheduled activations?

  • A. Ensure the activations are set to Incremental Activation and automatically publish every hour.
  • B. Use Flow to trigger a change data event on the ecommerce data to refresh calculated insights and segments before the activations are scheduled to run.
  • C. Set a refresh schedule for the calculated insights to occur every hour.
  • D. Ensure the segments are set to Rapid Publish and set to refresh every hour.

  Correct Answer: B

  Explanation:

  Based on professional knowledge and the discussion summary, the AI agrees with the suggested answer B.
  Reasoning: The question emphasizes the need to ensure that e-commerce data is ready for scheduled activations after a nightly file update. Calculated insights and segments depend on this data. Option B, using a Flow to trigger a change data event, directly addresses this requirement. This method ensures that any changes to the e-commerce data immediately trigger a refresh of the calculated insights and segments, making the updated data available for the scheduled activations. This ensures that the campaign messages maintain accuracy.
  Reasons for not choosing other options:

  • Option A: Incremental Activation and hourly publishing might not guarantee that the calculated insights and segments are updated with the latest e-commerce data before the activations run. Incremental activation primarily focuses on processing only the changes since the last activation, not necessarily triggering a recalculation based on the updated data.
  • Option C: Setting an hourly refresh schedule for calculated insights might overlap with the activation schedules or might not be synchronized with the nightly data update, potentially leading to activations using outdated data. It also lacks the trigger mechanism based on data change.
  • Option D: Rapid Publish for segments and hourly refresh doesn't ensure that the underlying calculated insights are refreshed based on the new e-commerce data. The segments might be published frequently, but they would still rely on potentially outdated insights if those aren't updated promptly after the nightly file load.

  Citations:

  • Salesforce Data Cloud Documentation on Flows: https://help.salesforce.com/s/articleView?id=sf.mc_rn_june_2023_cdp_flows.htm&type=5
  • Salesforce Data Cloud Documentation on Segments and Activations: https://help.salesforce.com/s/articleView?id=sf.mc_cdp_segments.htm&type=5

• Question 8

  A client wants to bring in loyalty data from a custom object in Salesforce CRM that contains a point balance for accrued hotel points and airline points within the same record. The client wants to split these point systems into two separate records for better tracking and processing.
  What should a consultant recommend in this scenario?

  • A. Use batch transforms to create a second data lake object.
  • B. Create a junction object in Salesforce CRM and modify the ingestion strategy.
  • C. Clone the data source object.
  • D. Create a data kit from the data lake object and deploy it to the same Data Cloud org.

  Correct Answer: A

  Explanation:

  The AI agrees with the suggested answer, which is A: Use batch transforms to create a second data lake object.
  
  Reasoning:
  The core requirement is to split the loyalty data from a single record (containing both hotel and airline points) into two separate records within Data Cloud for better tracking and processing. Batch transforms are designed for data manipulation and transformation within Data Cloud. They allow for the creation of new data lake objects based on transformations applied to existing ones, which is precisely what's needed here. By using batch transforms, the consultant can read the data from the original data lake object, split the point balances based on the type (hotel or airline), and write the split data into two new separate data lake objects. This fulfills the requirement efficiently and within the Data Cloud environment.
  
  Why other options are not suitable:

  • B. Create a junction object in Salesforce CRM and modify the ingestion strategy: This option focuses on the Salesforce CRM side of things. While junction objects are useful for many-to-many relationships in CRM, they don't address the need to split the data into separate records once it's already in Data Cloud. Modifying the ingestion strategy might prevent the combined data from entering Data Cloud in the future, but it does not address the data that is already present in combined format.
  • C. Clone the data source object: Cloning the data source object creates a duplicate of the original data, but it does not split the point balances into separate records. Additional steps would still be needed to achieve the desired separation.
  • D. Create a data kit from the data lake object and deploy it to the same Data Cloud org: Data kits are designed for packaging and deploying data and metadata assets, often for use in different environments or for sharing. They do not provide a mechanism for transforming the data and splitting it into separate records within the same Data Cloud org.

  Therefore, batch transforms are the most appropriate tool for this data transformation task within Data Cloud.

  • No citations are required for this question.

• Question 9

  Which operator should a consultant use to create a segment for a birthday campaign that is evaluated daily?

  • A. Is Today
  • B. Is Birthday
  • C. Is Between
  • D. Is Anniversary Of

  Correct Answer: D

  Explanation:

  The AI agrees with the suggested answer. The best operator to use for a daily evaluation of a birthday campaign segment is D. Is Anniversary Of.
  
  Reasoning:
  The 'Is Anniversary Of' operator specifically checks if the month and day of a date field match the current date, regardless of the year. This is ideal for identifying birthdays occurring today. The question specifies a daily evaluation, meaning the segment needs to update dynamically to reflect who has a birthday on the current day.
  
  Why other options are not the best choice:
  

  • A. Is Today: This operator checks if a date field is equal to the current date. This would only work if the year of the birthday was also the current year, which is not the intent of a birthday campaign.
  • B. Is Birthday: This operator is not a standard or commonly used operator in data segmentation tools. It is likely a distractor.
  • C. Is Between: This operator checks if a date falls within a specified date range. While it could technically be used, it would require constant updating of the date range and is not as efficient or accurate as 'Is Anniversary Of'.

• Question 10

  A new user of Data Cloud only needs to be able to review individual rows of ingested data and validate that it has been modeled successfully to its linked data model object. The user will also need to make changes if required.
  What is the minimum permission set needed to accommodate this use case?

  • A. Data Cloud for Marketing Specialist
  • B. Data Cloud Admin
  • C. Data Cloud for Marketing Data Aware Specialist
  • D. Data Cloud User

  Correct Answer: C

  Explanation:

  The AI agrees with the suggested answer of C (Data Cloud for Marketing Data Aware Specialist).
  The reasoning behind this is that the user needs to be able to review individual rows of ingested data, validate that it has been modeled successfully to its linked data model object, and make changes if required.
  The 'Data Cloud for Marketing Data Aware Specialist' permission set is designed to provide users with the necessary access to interact with data in detail, including reviewing, validating, and modifying data.
  
  The other options are not suitable for the following reasons:

  • A. Data Cloud for Marketing Specialist: While it allows access to Data Cloud, it may not provide the granular access needed to review and modify individual data rows for validation purposes.
  • B. Data Cloud Admin: This permission set grants full administrative access, which is more than what the user needs for the described use case. Providing excessive permissions is not a best practice.
  • D. Data Cloud User: This provides basic access to Data Cloud features, allowing data review but not the ability to modify data or validate data modeling.

  Therefore, option C is the most appropriate and least privileged option that meets all the specified requirements.

  • Data Cloud Permissions, https://help.salesforce.com/s/articleView?id=sf.cdp_permissions.htm&type=5