Question # 1
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.
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” — Main Generic Entity Attribute
“Opportunity Count” — Generic Custom Metric
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 11th. What is the number of ‘opportunities in the Confirmed Interest stage? | | A. 3 | | B. 1 | | C. 0 | | D. 2 |
C. 0
Explanation: Based on the data provided and the date filter set to January 11th, there are no records for 'Confirmed Interest' on that specific date in the Opportunity file. Thus, the number of opportunities in the 'Confirmed Interest' stage for January 11th would be zero(0).
In Salesforce Marketing Cloud Intelligence, when creating pivot tables, the data is aggregated based on the selected filters. If no records meet the filter criteria, the result for that category would be zero. The answer is supported by best practices in data analysis and reporting within Salesforce Marketing Cloud Intelligence, where date filters are applied to segment and analyze data.
Question # 3
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:
The mapping is as follows:
Day — Day
web_site_key —> Main Generic Entity Key web_site_name —> Main Generic Entity Name Web_site_source —> Main Generic Entity Attribute 01 Page Views — Generic Metric 1
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested? | | A. 1 | | B. 0 | | C. 3 | | D. 4 |
D. 4
Explanation: With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and'Web_site_source'. As 'Day' is mapped to 'Day', 'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and 'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'.
Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.
Question # 4
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 assume that the file is mapped in the OPPORTUNITIES Data Stream type with the following mapping:
“Day” — “Created Date” “Opportunity Key” + Opportunity Key
“Opportunity Stage” — Opportunity Stage
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 11th. What is the number of‘opportunitiesin the Confirmed Interest stage? | | A. 2 | | B. 1 | | C. 3 | | D. 0 |
D. 0
Explanation: pivot table is filtered on January 11th, we refer to the Opportunity file and see that there are no records for January 11th. Thus, there would be zero opportunities in the Confirmed Interest stage on that date. The Salesforce Marketing Cloud Intelligence's pivot table feature allows for the display of counts of entities based on the filtered criteria, which in this scenario would show zero since no records exist for the filtered date. Reference: Salesforce Marketing Cloud Intelligence documentation on pivot table functionalities.
Question # 5
| What is the relationship between “Media Buy Key” and “Campaign Key”? | | A. Many-to-one (one Campaign Key has many Media Buy Keys) | | B. Many-to-many | | C. One-to-many (one Media Buy Key has many Campaign Keys) | | D. One-to-one |
A. Many-to-one (one Campaign Key has many Media Buy Keys)
Explanation: Typically, 'Campaign Key' is a unique identifier for a specific marketing campaign, while 'Media Buy Key' refers to the purchases of advertising space associated with that campaign. A campaign can have multiple media buys, so the relationship is many- to-one, with many media buys (Media Buy Keys) associated with a single campaign (Campaign Key).
Question # 6
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 # 7
An Implementation engineer is requested to create anew harmonization field 'Offer’and apply the following logic:
The implementation engineer to use the Harmonization Center. Which of the below actions can help implement the new dimension ‘Offer? | | A. Two separate patterns (filtered by Linkedin or AdRoll sources) Within Google Analytics' mapping A formula that reflects the logic above will be populated within a Web Analytics Site custom attribute Another pattern to be created for the newly Web Analytics Site custom attribute (filtered by Google Analytics source). A total of 3 patterns. | | B. Two separate patterns (filtered by Linkedin or AdRoll sources) Within Google Analytics' mapping: A formula that reflects the logic above will be populated within a Campaign custom attribute. Another pattern to be created for the newly campaign attribute (filtered by Google Analytics source). A total of 3 patterns | | C. Two separate patterns (filtered by Linkedin or AdRoll sources). Another single pattern for Campaign Name (filtered by Google Analytics source). A total of 3 patterns. | | D. Two separate patterns (filtered by Linkedln or AdRoll sources). Another single pattern for Web Analytics Site Source (filtered by Google Analytics source), extracting all three positions A total of 3 patterns. |
B. Two separate patterns (filtered by Linkedin or AdRoll sources) Within Google Analytics' mapping: A formula that reflects the logic above will be populated within a Campaign custom attribute. Another pattern to be created for the newly campaign attribute (filtered by Google Analytics source). A total of 3 patterns
Explanation: To implement the new harmonization field 'Offer', the implementation engineer would create two separate harmonization patterns for LinkedIn and AdRoll sources, extracting the 'Campaign Name' using the specified delimiter and position. Then, within Google Analytics' mapping, a custom attribute for the 'Campaign' would be created to apply the formula logic based on the source. This allows for the harmonization of campaign data across different platforms, ensuring consistency in the reporting and analysis within Marketing Cloud Intelligence. The total patterns required would be three, one for each data source involved.
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