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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Harmonization | 3% | - Cross-source alignment - Pros and cons of harmonization approaches - Standardization methods |
| Topic 2: Calculated Fields and Functions | 5% | - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas - Custom calculations |
| Topic 3: General Functionalities | 13% | - Basic concepts and terminology - Navigation and interface - Core platform capabilities |
| Topic 4: Data Mapping and Modeling | 5% | - Data structure and relationships - Source of truth configuration - Dimensions and measures |
| Topic 5: Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Topic 6: Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Topic 7: Data Integration and Ingestion | 12% | - Refresh schedules and data flow - API and file-based import - Data streams and connectors |
| Topic 8: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 9: Data Permissions and Governance | 5% | - Parent-child data setups - User access and roles - Data update controls |
| Topic 10: Dashboards and Visualization | 15% | - Dashboard design and layout - KPI and metric display - Chart types and usage |
| Topic 11: Reporting and Analytics | 12% | - Attribution models - Insight generation and interpretation - Report creation and scheduling |
| Topic 12: Data Quality and Cleansing | 7% | - Error handling and correction - Duplicate management - Validation rules |
| Topic 13: Datasets and Data Views | 10% | - Filtering and segmentation - View configuration and sharing - Dataset creation and management |
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NEW QUESTION # 17
An implementation engineer has been provided with 4 different source files: 03m 48s
1. Twitter Ads ~
2. Creative Classification
3. Placement Classification
4, Campaign Category Classification
The main source is Twitter Ads (which includes various fields and KPIs), and the rest are classification files that connect to Twitter Ads and enrich different fields within it.
The connections between the files are described as follows:
1st Party Creative Classification
File structure/headers:
Creative ID - links back to Creative Key (Twitter Ads)
1st Party Placement Classification by
File structure/headers:
Answer: D
Explanation:
In Salesforce Marketing Cloud Intelligence, connections between source files and classification files are established through common keys that link data records. For this scenario:
The "1st Party Creative Classification" file has a "Creative ID" field which corresponds to the "Creative Key" in the "Twitter Ads" data. This link enables enrichment of Twitter Ads data with creative classification details.
The "1st Party Placement Classification" file will contain a "Placement ID" that connects to a corresponding field in the "Twitter Ads" data, enabling the enrichment of placement classification details.
Option A appears to accurately depict this setup where data streams for "Creative Classification" and "Placement Classification" are connected to the "Twitter Ads" data stream using the "Creative ID" and "Placement ID", respectively. This structure allows for the enhancement of the main Twitter Ads data with additional classification information.
NEW QUESTION # 18
What are unstable measurements?
Answer: C
Explanation:
Unstable measurements refer to metrics that are not aggregated in a standard manner across different grains of data, which can result in inconsistent or unpredictable results when reporting across different dimensions or time frames.
Option C describes a scenario where measurements have manual (Not Auto) aggregation settings, meaning they do not automatically adjust to the aggregation level of the report. Combined with a Granularity setting of 'None', this can lead to instability because the metric isn't bound to a specific granularity, which can cause data inconsistencies or misinterpretations when analyzed at varying levels of detail.
NEW QUESTION # 19
An Implementation engineer is requested to create a new 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?
Answer: D
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.
NEW QUESTION # 20
A client has integrated the following files:
File A:
File B:
The client would like to link the two files in order to view the two KPIs ('Tasks Completed' and 'Tasks Assigned) alongside 'Employee Name' and/or
'Squad'.
The client set the following properties:
+ File A is set as the Parent data stream
* Both files were uploaded to a generic data stream type.
* Override Media Buy Hierarchies is checked for file A.
* The 'Data Updates Permissions' set for file B is 'Update Attributes and Hierarchy'.
When filtering on the entire date range (1-30/8), and querying employee ID, Name and Squad with the two measurements - what will the result look like?
Answer: D
Explanation:
In Marketing Cloud Intelligence, when linking two data streams, the parent data stream (File A) provides the main structure. Since 'Override Media Buy Hierarchies' is checked for File A, the hierarchies from File B will be aligned with File A. Given 'Data Updates Permissions' set for file B as 'Update Attributes and Hierarchy', this means that attributes and hierarchy will be updated in the parent file based on the child file (File B), but the child file's metrics won't be associated with the parent file's date.
Hence, when filtering on the entire date range (1-30/8), the resulting view will align with the structure of the parent data stream, showing the KPIs ('Tasks Completed' from File A and 'Tasks Assigned' from File B) alongside the employee names and squads from the respective files. Since the employee IDs align, the data can be linked properly. However, since the dates do not align (File A data is from 01/08/2019 and File B from 15/08/2019), only attributes from File B will be updated without date association.
The result will look like Option C, where the employee names are corrected based on File B's data, the squads are added from File B, and the tasks_completed and tasks_assigned are displayed from their respective files. The tasks_assigned from File B are shown without date association as File B's date doesn't match with File A's.
NEW QUESTION # 21
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 # 22
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