This page was exported from Exam for engine [ http://blog.test4engine.com ] Export date:Mon Nov 18 2:21:50 2024 / +0000 GMT ___________________________________________________ Title: Salesforce Data-Cloud-Consultant Exam Questions (Updated 2024) 100% Real Question Answers [Q31-Q52] --------------------------------------------------- Salesforce Data-Cloud-Consultant Exam Questions (Updated 2024) 100% Real Question Answers Pass Salesforce Data-Cloud-Consultant Exam Quickly With Test4Engine Q31. 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 2 answers  Review data transformations to ensure they’re run after calculated insights.  Review calculated insights to make sure they’re run before segments are refreshed.  Review segments to ensure they’re refreshed after the data is ingested.  Review calculated insights to make sure they’re run after the segments are refreshed. ExplanationThe correct answer is B and C because calculated insights and segments are both dependent on the data ingestion process. Calculated insights are derived from the data model objects and segments are subsets of data model objects that meet certain criteria. Therefore, both of them need to be updated after the data is ingested to reflect the latest changes. Data transformations are optional steps that can be applied to the data streams before they are mapped to the data model objects, so they are not relevant to the issue. Reviewing calculated insights to make sure they’re run after the segments are refreshed (option D) is also incorrect because calculated insights are independent of segments and do not need to be refreshed after them. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Calculated Insights, SegmentsQ32. 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?  Create a formula field to standardize the format.  Edit and update the data in the source system prior to sending to Data Cloud.  Assign the PhoneNumber field type when creating the data stream.  Create a calculated insight after ingestion. 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 QuestionsQ33. Northern Trail Outfitters wants to use some of its Marketing Cloud data in Data Cloud.Which engagement channel data will require custom integration?  SMS  Email  CloudPage  Mobile push CloudPage is a web page that can be personalized and hosted by Marketing Cloud. It is not one of the standard engagement channels that Data Cloud supports out of the box. To use CloudPage data in Data Cloud, a custom integration is required. The other engagement channels (SMS, email, and mobile push) are supported by Data Cloud and can be integrated using the Marketing Cloud Connector or the Marketing Cloud API. References: Data Cloud Overview, Marketing Cloud Connector, Marketing Cloud APIQ34. Which consideration related to the way Data Cloud ingests CRM data is true?  CRM data cannot be manually refreshed and must wait for the next scheduled synchronization,  The CRM Connector’s synchronization times can be customized to up to 15-minute intervals.  Formula fields are refreshed at regular sync intervals and are updated at the next full refresh.  The CRM Connector allows standard fields to stream into Data Cloud in real time. ExplanationThe correct answer is D. The CRM Connector allows standard fields to stream into Data Cloud in real time.This means that any changes to the standard fields in the CRM data source are reflected in Data Cloud almost instantly, without waiting for the next scheduled synchronization. This feature enables Data Cloud to have the most up-to-date and accurate CRM data for segmentation and activation1.The other options are incorrect for the following reasons:* A. CRM data can be manually refreshed at any time by clicking the Refresh button on the data stream detail page2. This option is false.* B. The CRM Connector’s synchronization times can be customized to up to 60-minute intervals, not15-minute intervals3. This option is false.* C. Formula fields are not refreshed at regular sync intervals, but only at the next full refresh4. A full refresh is a complete data ingestion process that occurs once every 24 hours or when manually triggered.This option is false.References:* 1: Connect and Ingest Data in Data Cloud article on Salesforce Help* 2: Data Sources in Data Cloud unit on Trailhead* 3: Data Cloud for Admins module on Trailhead* 4: [Formula Fields in Data Cloud] unit on Trailhead* : [Data Streams in Data Cloud] unit on TrailheadQ35. A consultant needs to package Data Cloud components from oneorganization to another.Which two Data Cloud components should the consultant include in adata kit to achieve this goal?Choose 2 answers  Data model objects  Segments  Calculated insights  Identity resolution rulesets 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 segments1.* 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 CloudQ36. Cumulus Financial created a segment called High Investment Balance Customers. This is a foundational segment that includes several segmentation criteria the marketing team should consistently use.Which feature should the consultant suggest the marketing team use to ensure this consistency when creating future, more refined segments?  Create new segments using nested segments.  Create a High Investment Balance calculated insight.  Package High Investment Balance Customers in a data kit.  Create new segments by cloning High Investment Balance Customers. ExplanationNested segments are segments that include or exclude one or more existing segments. They allow the marketing team to reuse filters and maintain consistency in their data by using an existing segment to build a new one. For example, the marketing team can create a nested segment that includes High Investment Balance Customers and excludes customers who have opted out of email marketing. This way, they can leverage the foundational segment and apply additional criteria without duplicating the rules. The other options are not the best features to ensure consistency because:* B. A calculated insight is a data object that performs calculations on data lake objects or CRM data and returns a result. It is not a segment and cannot be used for activation or personalization.* C. A data kit is a bundle of packageable metadata that can be exported and imported across Data Cloud orgs. It is not a feature for creating segments, but rather for sharing components.* D. Cloning a segment creates a copy of the segment with the same rules and filters. It does not allow the marketing team to add or remove criteria from the original segment, and it may create confusion and redundancy. References: Create a Nested Segment – Salesforce, Save Time with Nested Segments (Generally Available) – Salesforce, Calculated Insights – Salesforce, Create and Publish a Data Kit Unit | Salesforce Trailhead, Create a Segment in Data Cloud – SalesforceQ37. 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 2 answers  Review data transformations to ensure they’re run after calculated insights.  Review calculated insights to make sure they’re run before segments are refreshed.  Review segments to ensure they’re refreshed after the data is ingested.  Review calculated insights to make sure they’re run after the segments are refreshed. The correct answer is B and C because calculated insights and segments are both dependent on the data ingestion process. Calculated insights are derived from the data model objects and segments are subsets of data model objects that meet certain criteria. Therefore, both of them need to be updated after the data is ingested to reflect the latest changes. Data transformations are optional steps that can be applied to the data streams before they are mapped to the data model objects, so they are not relevant to the issue. Reviewing calculated insights to make sure they’re run after the segments are refreshed (option D) is also incorrect because calculated insights are independent of segments and do not need to be refreshed after them. References: Salesforce Data Cloud Consultant Exam Guide, Data Ingestion and Modeling, Calculated Insights, SegmentsQ38. What does the Source Sequence reconciliation rule do in identity resolution?  Includes data from sources where the data is most frequently occurring  Identifies which individual records should be merged into a unified profile by setting a priority for specific data sources  Identifies which data sources should be used in the process of reconcillation by prioritizing the most recently updated data source  Sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name ExplanationThe Source Sequence reconciliation rule sets the priority of specific data sources when building attributes in a unified profile, such as a first or last name. This rule allows you to define which data source should be used as the primary source of truth for each attribute, and which data sources should be used as fallbacks in case the primary source is missing or invalid. For example, you can set the Source Sequence rule to use data from Salesforce CRM as the first priority, data from Marketing Cloud as the second priority, and data from Google Analytics as the third priority for the first name attribute. This way, the unified profile will use the first name value from Salesforce CRM if it exists, otherwise it will use the value from Marketing Cloud, and so on. This rule helps you to ensure the accuracy and consistency of the unified profile attributes across different data sources. References: Salesforce Data Cloud Consultant Exam Guide, Identity Resolution, Reconciliation RulesQ39. Which data model subject area should be used for any Organization, Individual, or Member in the Customer360 data model?  Engagement  Membership  Party  Global Account The data model subject area that should be used for any Organization, Individual, or Member in the Customer360 data model is the Party subject area. The Party subject area defines the entities that are involved in any business transaction or relationship, such as customers, prospects, partners, suppliers, etc. The Party subject area contains the following data model objects (DMOs):* Organization: A DMO that represents a legal entity or a business unit, such as a company, a department, a branch, etc.* Individual: A DMO that represents a person, such as a customer, a contact, a user, etc.* Member: A DMO that represents the relationship between an individual and an organization, such as an employee, a customer, a partner, etc.The other options are not data model subject areas that should be used for any Organization, Individual, or Member in the Customer 360 data model. The Engagement subject area defines the actions that people take, such as clicks, views, purchases, etc. The Membership subject area defines the associations that people have with groups, such as loyalty programs, clubs, communities, etc. The Global Account subject area defines the hierarchical relationships between organizations, such as parent-child, subsidiary, etc.References:* Data Model Subject Areas* Party Subject Area* Customer 360 Data ModelQ40. Northern Trail Outfitters unifies individuals in its Data Cloud instance.Which threefeatures ca e consultant use to validate the data on a unified profile?Choose 3 answers  Identity Resolution  Query APL  Data Explorer  Profile Explorer  Data Actions ExplanationTo validate the data on a unified profile, the consultant can use the following features:* Identity Resolution: This feature allows the consultant to view and edit the identity resolution rulesets that determine how individuals are unified from different data sources1.* Data Explorer: This feature allows the consultant to browse and filter the unified profiles and view their attributes, segments, and activities2.* Profile Explorer: This feature allows the consultant to drill down into a specific unified profile and view its details, such as source records, identity graph, calculated insights, and data actions3. References:* 1: Identity Resolution in Data Cloud* 2: Data Explorer in Data Cloud* 3: Profile Explorer in Data CloudQ41. A healthcare client wants to make use of identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII).Which matching rule criteria should a consultant recommend for the most accurate matching results?  Party Identification on Patient ID  Exact Last Name and Emil  Email Address and Phone  Fuzzy First Name, Exact Last Name, and Email Identity resolution is the process of linking data from different sources into a unified profile of a customer or an individual. Identity resolution uses matching rules to compare the attributes of different records and determine if they belong to the same person. Matching rules can be based on exact or fuzzy matching of various attributes, such as name, email, phone, address, or custom identifiers. A healthcare client who wants to use identity resolution, but does not want to risk unifying profiles that may share certain personally identifying information (PII), such as name or email, should use a matching rule criteria that is based on a unique and reliable identifier that is specific to the healthcare domain. One such identifier is the patient ID, which is a unique number assigned to each patient by a healthcare provider or system. By using the party identification on patient ID as a matching rule criteria, the healthcare client can ensure that only records that have the same patient ID are matched and unified, and avoid false positives or false negatives that may occur due to common or similar names or emails. The party identification on patient ID is also a secure and compliant way of handling sensitive healthcare data, as it does not expose or share any PII that may be subject to data protection regulations or standards. References: Configure Identity Resolution Rulesets, A framework of identity resolution: evaluating identity attributes and methodsQ42. What does the Ignore Empty Value option do in identity resolution?  Ignores empty fields when running any custom match rules  Ignores empty fields when running reconciliation rules  Ignores Individual object records with empty fields when running identity resolution rules  Ignores empty fields when running the standard match rules The Ignore Empty Value option in identity resolution allows customers to ignore empty fields when running reconciliation rules. Reconciliation rules are used to determine the final value of an attribute for a unified individual profile, based on the values from different sources. The Ignore Empty Value option can be set to true or false for each attribute in a reconciliation rule. If set to true, the reconciliation rule will skip any source that has an empty value for that attribute and move on to the next source in the priority order. If set to false, the reconciliation rule will consider any source that has an empty value for that attribute as a valid source and use it to populate the attribute value for the unified individual profile.The other options are not correct descriptions of what the Ignore Empty Value option does in identity resolution. The Ignore Empty Value option does not affect the custom match rules or the standard match rules, which are used to identify and link individuals across different sources based on their attributes. The Ignore Empty Value option also does not ignore individual object records with empty fields when running identity resolution rules, as identity resolution rules operate on the attribute level, not the record level.References:* Data Cloud Identity Resolution Reconciliation Rule Input* Configure Identity Resolution Rulesets* Data and Identity in Data CloudQ43. Northern Trail Outfitters is using the Marketing Cloud Starter Data Bundles to bring Marketing Cloud data into Data Cloud.What are two of the available datasets in Marketing Cloud Starter Data Bundles?Choose 2 answers  Personalization  MobileConnect  Loyalty Management  MobilePush ExplanationThe Marketing Cloud Starter Data Bundles are predefined data bundles that allow you to easily ingest data from Marketing Cloud into Data Cloud1. The available datasets in Marketing Cloud Starter Data Bundles are Email, MobileConnect, and MobilePush2. These datasets contain engagement events and metrics from different Marketing Cloud channels, such as email, SMS, and push notifications2. By using these datasets, you can enrich your Data Cloud data model with Marketing Cloud data and create segments and activations based on your marketing campaigns and journeys1. The other options are incorrect because they are not available datasets in Marketing Cloud Starter Data Bundles. Option A is incorrect because Personalization is not a dataset, but a feature of Marketing Cloud that allows you to tailor your content and messages to your audience3. Option C is incorrect because Loyalty Management is not a dataset, but a product of Marketing Cloud that allows you to create and manage loyaltyprograms for your customers4. References: Marketing Cloud Starter Data Bundles in Data Cloud, Connect Your Data Sources, Personalization in Marketing Cloud, Loyalty Management in Marketing CloudQ44. What is Data Cloud’s primary value to customers?  To provide a unified view of a customer and their related data  To connect all systems with a golden record  To create a single source of truth for all anonymous data  To create personalized campaigns by listening, understanding, and acting on customer behavior ExplanationData Cloud is a platform that enables you to activate all your customer data across Salesforce applications and other systems. Data Cloud allows you to create a unified profile of each customer by ingesting, transforming, and linking data from various sources, such as CRM, marketing, commerce, service, and external data providers. Data Cloud also provides insights and analytics on customer behavior, preferences, and needs, as well as tools to segment, target, and personalize customer interactions. Data Cloud’s primary value to customers is to provide a unified view of a customer and their related data, which can help you deliver better customer experiences, increase loyalty, and drive growth. References: Salesforce Data Cloud, When Data Creates Competitive AdvantageQ45. Cloud Kicks wants to be able to build a segment of customers who have visited its website within the previous7 days.Which filter operator on the Engagement Date field fits this use case?  Is Between  Greater than Last Number of  Next Number of Days  Last Number of Days ExplanationThe filter operator Last Number of Days allows you to filter on date fields using a relative date range that specifies the number of days before today. For example, you can use this operator to filter on customers who have visited your website in the last 7 days, or the last 30 days, or any number of days you want. This operator is useful for creating dynamic segments that update automatically based on the current date12. References:* Relative Date Filter Reference* Create Filtered SegmentsQ46. Cumulus Financial wants to be able to track the daily transaction volume of each of its customers in real time and send out a notification as soon as it detects volume outside a customer’s normal range.What should a consultant do to accommodate this request?  Use a calculated insight paired with a flow.  Use streaming data transform with a flow.  Use a streaming insight paired with a data action  Use streaming data transform combined with a data action. A streaming insight is a type of insight that analyzes streaming data in real time and triggers actions based on predefined conditions. A data action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. By using a streaming insight paired with a data action, a consultant can accommodate Cumulus Financial’s request to track the daily transaction volume of each customer and send out a notification when the volume is outside the normal range. A calculated insight is a type of insight that performs calculations on data in a data space and stores the results in a data extension. A streaming data transform is a type of data transform that applies transformations to streaming data in real time and stores the results in a data extension. A flow is a type of automation that executes a series of actions when triggered by an event, a schedule, or another flow. None of these options can achieve the same functionality as a streaming insight paired with a data action. References: Use Insights in Data Cloud Unit, Streaming Insights and Data Actions Use Cases, Streaming Insights and Data Actions Limits and BehaviorsQ47. Cloud Kicks received a Request to be Forgotten by a customer.In which two ways should a consultant use Data Cloud to honor this request?Choose 2 answers  Delete the data from the incoming data stream and perform a full refresh.  Add the Individual ID to a headerless file and use the delete from file functionality.  Use Data Explorer to locate and manually remove the Individual.  Use the Consent API to suppress processing and delete the Individual and related records from source data streams. ExplanationTo honor a Request to be Forgotten by a customer, a consultant should use Data Cloud in two ways:* Add the Individual ID to a headerless file and use the delete from file functionality. This option allows the consultant to delete multiple Individuals from Data Cloud by uploading a CSV file with their IDs1. The deletion process is asynchronous and can take up to 24 hours to complete1.* Use the Consent API to suppress processing and delete the Individual and related records from source data streams. This option allows the consultant to submit a Data Deletion request for an Individual profile in Data Cloud using the Consent API2. A Data Deletion request deletes the specified Individual entity and any entities where a relationship has been defined between that entity’s identifying attribute and the Individual ID attribute2. The deletion process is reprocessed at 30, 60, and 90 days to ensure a full deletion2. The other options are not correct because:* Deleting the data from the incoming data stream and performing a full refresh will not delete the existing data in Data Cloud, only the new data from the source system3.* Using Data Explorer to locate and manually remove the Individual will not delete the related records from the source data streams, only the Individual entity in Data Cloud. References:* Delete Individuals from Data Cloud* Requesting Data Deletion or Right to Be Forgotten* Data Refresh for Data Cloud* [Data Explorer]Q48. A consultant is setting up a data stream with transactional data,Which field typeshould the consultant choose toensure that leadingzeros in the purchase order number are preserved?  Text  Number  Decimal  Serial ExplanationThe field type Text should be chosen to ensure that leading zeros in the purchase order number are preserved.This is because text fields store alphanumeric characters as strings, and do not remove any leading or trailing characters. On the other hand, number, decimal, and serial fields store numeric values as numbers, and automatically remove any leading zeros when displaying or exporting the data123. Therefore, text fields are more suitable for storing data that needs to retain its original format, such as purchase order numbers, zip codes, phone numbers, etc. References:* Zeros at the start of a field appear to be omitted in Data Exports* Keep First ‘0’ When Importing a CSV File* Import and export address fields that begin with a zero or contain a plus symbolQ49. 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?  Mew dimensions can be added.  Existing dimensions can be removed.  Existing measures can be removed.  Mew measures can be added. ExplanationA 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 insight12:* 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.Q50. When creating a segment on an individual, what is the result of using two separate containers linked by an AND as shown below?GoodsProduct | Count | At Least | 1Color | Is Equal To | redANDGoodsProduct | Count | At Least | 1PrimaryProductCategory | Is Equal To | shoes  Individuals who purchased at least one of any red’ product and also purchased at least one pair of ‘shoes’  Individuals who purchased at least one ‘red shoes’ as a single line item in a purchase  Individuals who made a purchase of at least one ‘red shoes’ and nothing else  Individuals who purchased at least one of any ‘red’ product or purchased at least one pair of‘shoes’ ExplanationWhen creating a segment on an individual, using two separate containers linked by an AND means that the individual must satisfy both the conditions in the containers. In this case, the individual must have purchased at least one product with the color attribute equal to ‘red’ and at least one product with the primary product category attribute equal to ‘shoes’. The products do not have to be the same or purchased in the same transaction. Therefore, the correct answer is A.The other options are incorrect because they imply different logical operators or conditions. Option B implies that the individual must have purchased a single product that has both the color attribute equal to ‘red’ and the primary product category attribute equal to ‘shoes’. Option C implies that the individual must have purchased only one product that has both the color attribute equal to ‘red’ and the primary product category attribute equal to ‘shoes’ and no other products. Option D implies that the individual must have purchased either one product with the color attribute equal to ‘red’ or one product with the primary product category attribute equal to ‘shoes’ or both, which is equivalent to using an OR operator instead of an AND operator.References:* Create a Container for Segmentation* Create a Segment in Data Cloud* Navigate Data Cloud SegmentationQ51. Which two common use cases can be addressed with Data Cloud?Choose 2 answers  Understand and act upon customer data to drive more relevant experiences.  Govern enterprise data lifecycle through a centralized set of policies and processes.  Harmonize data from multiple sources with a standardized and extendable data model.  Safeguard critical business data by serving as a centralized system for backup and disaster recovery. Data Cloud is a data platform that can help customers connect, prepare, harmonize, unify, query, analyze, and act on their data across various Salesforce and external sources. Some of the common use cases that can be addressed with Data Cloud are:* Understand and act upon customer data to drive more relevant experiences. Data Cloud can help customers gain a 360-degree view of their customers by unifying data from different sources and resolving identities across channels. Data Cloud can also help customers segment their audiences, create personalized experiences, and activate data in any channel using insights and AI.* Harmonize data from multiple sources with a standardized and extendable data model. Data Cloud can help customers transform and cleanse their data before using it, and map it to a common data model that can be extended and customized. Data Cloud can also help customers create calculated insights and related attributes to enrich their data and optimize identity resolution.The other two options are not common use cases for Data Cloud. Data Cloud does not provide data governance or backup and disaster recovery features, as these are typically handled by other Salesforce or external solutions.References:* Learn How Data Cloud Works* About Salesforce Data Cloud* Discover Use Cases for the Platform* Understand Common Data Analysis Use CasesQ52. Cumulus Financial wants to be able to track the daily transaction volume of each of its customers in real time and send out anotification as soon as it detects volume outside a customer’s normal range.What should a consultant do to accommodate this request?  Use a calculated insight paired with a flow.  Use streaming data transform with a flow.  Use a streaming insight paired with a data action  Use streaming data transform combined with a data action. ExplanationA streaming insight is a type of insight that analyzes streaming data in real time and triggers actions based on predefined conditions. A data action is a type of action that executes a flow, a data action target, or a data action script when an insight is triggered. By using a streaming insight paired with a data action, a consultant can accommodate Cumulus Financial’s request to track the daily transaction volume of each customer and send out a notification when the volume is outside the normal range. A calculated insight is a type of insight that performs calculations on data in a data space and stores the results in a data extension. A streaming data transform is a type of data transform that applies transformations to streaming data in real time and stores the results in a data extension. A flow is a type of automation that executes a series of actions when triggered by an event, a schedule, or another flow. None of these options can achieve the same functionality as a streaming insight paired with a data action. References: Use Insights in Data Cloud Unit, Streaming Insights and Data Actions Use Cases, Streaming Insights and Data Actions Limits and Behaviors Loading … Real Salesforce Data-Cloud-Consultant Exam Questions [Updated 2024]: https://www.test4engine.com/Data-Cloud-Consultant_exam-latest-braindumps.html --------------------------------------------------- Images: https://blog.test4engine.com/wp-content/plugins/watu/loading.gif https://blog.test4engine.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-02-28 13:01:54 Post date GMT: 2024-02-28 13:01:54 Post modified date: 2024-02-28 13:01:54 Post modified date GMT: 2024-02-28 13:01:54