Question # 1
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 # 2
An implementation engineer has been asked by a client for assistance with the following problem:
The below dataset was ingested:
However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type’ is 4.
What could be the reason for this discrepancy? |
| A. The measurement ‘Clicks’ is set as a percentage. | | B. A mapping formula was populated, indicating not to bring Type! values. | | C. The aggregation function is set as AVG | | D. The aggregation function is set as LIFETIME |
C. The aggregation function is set as AVG
Explanation: The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is 8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. References: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.
Question # 3
An implementation engineer has been provided with the below dataset:
*Note: CPC = Cost per Click Formula: Cost / Clicks
Which action should an engineer take to successfully integrate CPC? |
| A. Populate the logic within a custom measurement. No need to change Aggregation. | | B. Unmap it, as Datorama will calculate it automatically. | | C. Populate the logic within a custom measurement. Set Aggregation to AVG. | | D. Populate the logic within a custom measurement. Set Aggregation to SUM. |
A. Populate the logic within a custom measurement. No need to change Aggregation.
Explanation: CPC (Cost per Click) is a calculated metric that should be created using a custom measurement based on the formula provided (Cost / Clicks). This calculation does not require a change in the aggregation setting because it is derived from other base metrics that are already aggregated appropriately. In Salesforce Marketing Cloud Intelligence, custom measurements are used to create new metrics from existing data points, and the system will use the underlying data's aggregation to perform the calculation. References: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.
Question # 4
A client's data consists of three data streams as follows:
* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.
Which data stream should be set as a parent? |
| A. Data Stream A | | B. Data Stream C | | C. Any of the data streams can technically be the parent | | D. Data Stream B |
B. Data Stream C
Explanation: Since Data Stream C is considered the source of truth for both dimensions and measurements, it should be set as the parent data stream. This is because the parent data stream is used as the primary source for hierarchical and attribute data within a parent-child relationship setup. As the source of truth, Data Stream C will provide the foundational data upon which the other streams can be aligned and will ensure consistency and accuracy across the linked data.
Question # 5
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 Interest stage? |
| A. 1 | | B. 3 | | C. 2 | | D. 0 |
D. 0
Explanation: Since the pivot table is filtered on January 11th and the provided Opportunity file does not show any records dated January 11th, there are zero opportunities in the Interest stage for that date. Salesforce Marketing Cloud Intelligence allows users to create pivot tables and filter data based on specific criteria, such as dates. In this case, the filter would exclude all rows that do not match the specified date, resulting in a count of zero for the Interest stage. This would apply to any stage since there are no records for January 11th. Reference can be made to Salesforce Marketing Cloud Intelligence documentation on filtering and pivot tables.
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