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| Section | Objectives |
|---|---|
| Data Harmonization and Data Model | - Data Classification
|
| Data Integration and Configuration | - Data Stream Types
|
| Reports and Activation | - Report Types
|
| Data Visualization and Dashboards | - Dashboard Design
|
| Marketing Intelligence Platform Fundamentals | - Platform Overview
|
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NEW QUESTION # 62
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?
Answer: A
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.
NEW QUESTION # 63
A client created a new KPI: CPS (Cost per Sign-up).
The new KIP is mapped within the data stream mapping, and is populated with the following logic: (Media Cost) / Sign-ups) As can be seen in the table below, CPS was created twice and was set with two different aggregations:
From looking at the table, what are the aggregation settings for each one of the newly created KPIs?
Answer: C
Explanation:
The KPI CPS (Cost per Sign-up) would be calculated by dividing the 'Media Cost' by 'Sign-ups'. The table indicates that CPS is set with two different aggregations. In option C, CPS #1 is set to 'AUTO', which allows the system to decide the best aggregation method based on the context. CPS #2 is set to 'SUM', which indicates that the individual costs per sign-up are summed up across multiple records to provide a total cost per sign-up.
NEW QUESTION # 64
A client has integrated data from Facebook Ads, Twitter Ads, and Google Ads in Marketing Cloud Intelligence. For each data source, the data follows a naming convention as shown below:
Facebook Ads Naming Convention - Campaign Name:
Camp|D_CampName#Market_Objective#TargetAge_TargetGender
Twitter Ads Naming Convention - Media Buy Name:
Market|TargetAge|Objective|OrderID
' 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.
In addition to the previous details, the client provides the following data sample:
Logic specification:
If a value is not present in the Validation List, return "Not Valid"
If a value is not present in the Classification File, return "Unclassified".
If the Harmonization center is used to harmonize the above data and files, what table will show the final output?
Answer: A
Explanation:
The correct table would be Option B.
The harmonization process would identify the 'Market' from the campaign or media buy name based on the delimiter and position rules specified in the naming conventions. The harmonized 'Market' would then be matched against the classification file and validation list. If a value does not match the validation list, it would return 'Not Valid', and if it's not present in the classification file, it would return 'Unclassified'. Option B is the only table showing the 'Not Valid' category which aligns with the logic specification provided.
NEW QUESTION # 65
An implementation engineer is requested to extract the second position
of the Campaign Name values.
The Campaign values consist of multiple delimiter types, as can be
seen in the following example:
Campaign Name: Ad15X2w&Delux_wal90
Desired value: Delux
Which three harmonization methods will achieve the desired outcome?
Answer: A,C,D
Explanation:
To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:
Calculated Dimensions: These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract segments of a string based on delimiters.
Patterns: This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.
Mapping Formula: Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly within data streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.
These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.
NEW QUESTION # 66
After uploading a standard file into Marketing Cloud intelligence via total Connect, you noticed that the number of rows uploaded (to the specific data stream) is NOT equal to the number of rows present in the source file. What are two resource that may cause this gap?
Answer: A,C
Explanation:
In Marketing Cloud Intelligence, discrepancies between the number of rows uploaded and the number of rows present in the source file can be caused by several factors. If all mapped measurements for a row are zero, that row may be excluded from the upload, as it does not contribute to the analytics. Additionally, if the main entity, which acts as the primary identifier for records, is not mapped, the system cannot correctly ingest the data as it lacks the necessary reference to organize and store the information.
NEW QUESTION # 67
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