Question # 1
A client has integrated data from Facebook Ads. Twitter ads, and Google ads in marketing Cloud intelligence. For each data source, the source, the data follows a naming convensions as …
Facebook Ads Naming Convention - Campaign Name:
CampID_CampName#Market_Object#object#targetAge_TargetGender Twitter Ads Naming Convention- Media Buy Name MarketTargeAgeObjectiveOrderID
Google ads Naming Convention-Media Buy Name: Buying_type_Market_Objective
The client wants to harmonize their data on the common fields between these two platforms (i.e. Market and Objective) using the Harmonization Center. Given the above information, which statement is correct regarding the ability to implement this request?
wet Me - Given the above information, which statement i 's Correct regarding the ability to implement this request? | | A. The clientWi-Fibe able to harmonize only Google Ads and Twitter Ads, as Facebook Ads naming convention contains mufti delimiters. | | B. it is not possible to do this, as the naming conventions are different | | C. This is not possible as the naming conventions are in different fields (Campaign Name and Placement Name) | | D. The client will be able to do this and it will require building three patterns. |
D. The client will be able to do this and it will require building three patterns.
Explanation: Despite the different naming conventions, harmonization is possible using patterns in the Harmonization Center. By extracting the 'Market' and 'Objective' components from the naming conventions of each platform, three separate patterns would be created to map these common fields consistently across the data from Facebook Ads, Twitter Ads, and Google Ads.
Question # 2
A technical architect is provided with the logic and Opportunity file shown below:
The opportunity status logic is as follows:
For the opportunity stages “Interest”, “Confirmed Interest” and “Registered”, the status should be “Open”.
For the opportunity stage “Closed”, the opportunity status should be closed Otherwise, return null for the opportunity status.
Given the above file and logic and assuming that the file is mapped in a generic data stream type with the following mapping
“Day” — Standard “Day” field
“Opportunity Key” > Main Generic Entity Key
“Opportunity Stage” + Generic Entity Key 2
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 7th - 11th. Which option reflects the stage(s) the Opportunity key 123AA01 is associated with? | | A. Registered | | B. Interest & Registered | | C. Confirmed Interest & Registered | | D. Interest | | E. Confirmed Interest |
B. Interest & Registered
Explanation: Analyzing the Opportunity file with a filter set from January 7th to 11th, Opportunity Key '123AA01' appears under 'Interest' on January 6th and 8th, and under 'Registered' on January 10th. Therefore, during the specified date range, Opportunity Key '123AA01' is associated with both 'Interest' and 'Registered' stages. Salesforce Marketing Cloud Intelligence provides the capability to map and track opportunity stages over time, allowing for historical stage tracking and reporting. This answer aligns with the ability to use pivot tables to filter and display data by specific attributes and timeframes, as outlined in the Salesforce Marketing Cloud Intelligence documentation.
Question # 3
| Which three statements accurately describe the different data stream types in Marketing Cloud intelligence? | | A. Every data stream type includes the Medio Buy entity | | B. All data stream types consist of at least one entity | | C. All data stream types share at least one mutual measurement | | D. Each data stream type has Its own main entity | | E. Each data stream type has its own set of measurements |
B. All data stream types consist of at least one entity
D. Each data stream type has Its own main entity
E. Each data stream type has its own set of measurements
Question # 4
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing Insights and cross-channel marketing activity analysis. Below are details regarding the different data sources and the number of data streams required for each source.
Which three advantages does a client gain from using Calculated Dimensions as the harmonization method for creating the Objective field? | | A. Scalability - future data streams that will follow similar logic will be automatically harmonized. | | B. Data model restrictions - Calculated Dimensions do not need to adhere to Marketing Cloud Intelligence's data model | | C. Processing - creation of Calculated Dimensions will ease the processing time of the data streams it relates to | | D. Ease of Maintenance - the logic is written and populated in one centralized place | | E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database. |
A. Scalability - future data streams that will follow similar logic will be automatically harmonized.
D. Ease of Maintenance - the logic is written and populated in one centralized place
E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database.
Explanation: Scalability: Using Calculated Dimensions allows the client to apply the same harmonization logic to future data streams, ensuring consistency and reducing the need for individual adjustments.
Ease of Maintenance: With the logic centralized in Calculated Dimensions, any adjustments or updates are applied in one place, simplifying ongoing management.
Performance: Calculated Dimensions can improve dashboard performance because their values are pre-computed and stored, reducing the need for real-time calculations when loading dashboards.
Question # 5
Your client is interested in ingested the below file to a new generic data stream type:
The field ‘Meeting Code’ was mapped to the main entity key. ‘How should the ‘Room Number’ be mapped? | | A. An attribute of ‘Meeting Code’ | | B. A custom metric and set aggregation to AUTO | | C. A custom metric and set aggregation to SUM | | D. A separate entity key |
A. An attribute of ‘Meeting Code’
Explanation: In Marketing Cloud Intelligence, when a field is mapped to the main entity key, other related fields should be mapped as attributes of that key if they provide additional descriptors or details. Since 'Room Number' is related to 'Meeting Code', it would be an attribute of the' Meeting Code' entity, providing additional context to the meetings without serving as a metric or a separate entity key.
Question # 6
A client's data consists of three data streams as follows:
Data Stream A:
 | | A. Update Attributes | | B. Inherit Attributes and Hierarchies | | C. Update Attributes and Hierarchies | | D. It doesn't matter. As long as Data stream A is set as a Parent', the rest of the Data Updates Permissions are irrelevant. |
B. Inherit Attributes and Hierarchies
Explanation: For the client's data consisting of three data streams, setting Data Stream A as the Parent allows for inheriting attributes and hierarchies from it to the child data streams. This ensures consistency across the data streams, making it possible to analyze the data collectively, using the structure and attributes defined in the Parent data stream.
Question # 7
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activityanalysis. Below are details regarding the different data sources and the number of data streams required for each source.
What three advantages are gained when using Patterns & Data Classification as the harmonization method for creating the Objective field? | | A. Ease of Maintenance | | B. Performance (Performance when loading a dashboard page) | | C. Use of code | | D. Scalability | | E. Processing (processing time when loading relevant data streams) |
A. Ease of Maintenance
B. Performance (Performance when loading a dashboard page)
D. Scalability
Explanation: Patterns & Data Classification in Marketing Cloud Intelligence offer several advantages. These include:
Ease of Maintenance (A):Patterns allow for the standardization of data harmonization processes. Once set up, they can be easily maintained and adjusted as needed, without having to manipulate each data stream individually. Performance (B):By using patterns, data is classified and standardized at ingestion, which can improve the performance of dashboard page loading because the system does not need to perform complex, on-the-fly calculations or transformations.
Scalability (D):Patterns can be applied across multiple data streams consistently, allowing them to scale with the data. This means that as the amount of data grows or as new data sources are added, the same patterns can be reused, ensuring that the data remains harmonized.
Reference: Information is based on general knowledge of marketing data harmonization and standard practices as patterns and classification rules are common features in data platforms, extrapolated to fit the context of Salesforce Marketing Cloud Intelligence.
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