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Last Updated: Aug 13, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Harmonization | 3% | - Standardization methods - Pros and cons of harmonization approaches - Cross-source alignment |
| Topic 2: Calculated Fields and Functions | 5% | - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP - Custom calculations |
| Topic 3: Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Topic 4: General Functionalities | 13% | - Core platform capabilities - Basic concepts and terminology - Navigation and interface |
| Topic 5: Datasets and Data Views | 10% | - Filtering and segmentation - View configuration and sharing - Dataset creation and management |
| Topic 6: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 7: Data Permissions and Governance | 5% | - Parent-child data setups - User access and roles - Data update controls |
| Topic 8: Dashboards and Visualization | 15% | - Dashboard design and layout - Chart types and usage - KPI and metric display |
| Topic 9: Data Mapping and Modeling | 5% | - Dimensions and measures - Source of truth configuration - Data structure and relationships |
| Topic 10: Data Integration and Ingestion | 12% | - API and file-based import - Data streams and connectors - Refresh schedules and data flow |
| Topic 11: Data Quality and Cleansing | 7% | - Validation rules - Error handling and correction - Duplicate management |
| Topic 12: Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Topic 13: Reporting and Analytics | 12% | - Report creation and scheduling - Attribution models - Insight generation and interpretation |
1. Source 3:
Via the harmonization Center, the Client has created Patterns and applied a classification rule using source 2.
While performing QA, you have spotted that the final value of clicks for Product Group Ais 10, where it should've been i5.
How can an implementation engineer fix this discrepancy?
A) Leave the "Case Sensitive" checkbox in the data classification unchecked
B) Uncheck the "Case Sensitive" checkbox in the data classification
C) Toggle the 'Structure Compliant' OFF.
D) Upload both source 1 and 3 to the same data stream type in order to be able to generate Patterns from them.
2. 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.
When harmonizing the Objective field from within the data stream mapping, which advantage is gained?
A) Scalability
B) Ease of Maintenance
C) Performance (Performance when loading a dashboard page)
D) Ease of Setup
3. A client has provided you with sample files of their data from the following data sources:
1.Google Analytics
2.Salesforce Marketing Cloud
The link between these sources is on the following two fields:
Message Send Key
A portion of: web_site_source_key
Below is the logic the client would like to have implemented in Datorama:
For 'web site medium' values containing the word "email" (in all of its forms), the section after the "_" delimiter in 'web_site_source_key' is a 4 digit number, which matches the 'Message Send Key' values from the Salesforce Marketing Cloud file. Possible examples of this can be seen in the following table:
Google Analytics:
Salesforce Marketing Cloud:
The client's objective is to visualize the mutual key values alongside measurements from both files in a table.
In order to achieve this, what steps should be taken?
A) Create a Web Analytics Site Source custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
B) Create a Web Analytics Site custom attribute and populate it with the extraction logic. Create a Data Fusion between the newly created attribute and the Message Send Key.
C) Within both files, map the desired value to Custom Classification Key as follows Salesforce Marketing Cloud: map entire Message Key to Custom Classification Key.
Google Analytics: map the extraction logic to Custom Classification Key.
D) Upload the two files and create a Parent-Child relationship between them. The Override Media Buy Hierarchy checkbox is checked in Google Analytics.
4. An implementation engineer is requested to extract the first three-letter segment of the Campaign Name values.
For example:
Campaign Name: AFD@Mulop-1290
Desired outcome: AFD
Other examples:
Which formula will return the desired values?
A) LEFT(EXTRACT(csv[campaign_name'}/-',1),3)
B) EXTRACT(EXTRACT(csv['campaign_name]]/@',1),-,0)
C) EXTRACT(csv[campaign_name!;@',1)
D) LEFT(EXTRACT(csy['campaign_name]],~',0),3)
E) EXTRACT(csv[campaign_name'],-,0)
5. 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?
A)
B)
C)
D) 
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
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