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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Topic 2: Calculated Fields and Functions | 5% | - Custom calculations - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas |
| Topic 3: Data Mapping and Modeling | 5% | - Source of truth configuration - Dimensions and measures - Data structure and relationships |
| Topic 4: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 5: General Functionalities | 13% | - Core platform capabilities - Navigation and interface - Basic concepts and terminology |
| Topic 6: Data Harmonization | 3% | - Pros and cons of harmonization approaches - Standardization methods - Cross-source alignment |
| Topic 7: Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
| Topic 8: Reporting and Analytics | 12% | - Attribution models - Report creation and scheduling - Insight generation and interpretation |
| Topic 9: Data Quality and Cleansing | 7% | - Duplicate management - Validation rules - Error handling and correction |
| Topic 10: Datasets and Data Views | 10% | - View configuration and sharing - Filtering and segmentation - Dataset creation and management |
| Topic 11: Data Integration and Ingestion | 12% | - Data streams and connectors - Refresh schedules and data flow - API and file-based import |
| Topic 12: Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| Topic 13: Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
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NEW QUESTION # 34
A client's data consists of three data streams as follows:
Data Stream A:
* 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.
* Data Stream C was set as a 'Parent', and the 'Override Media Buy Hierarchy' checkbox is checked What should the Data Updates Permissions be set to for Data Stream B?
Answer: D
Explanation:
With Data Stream C set as the 'Parent' and 'Override Media Buy Hierarchy' checked:
The appropriate setting for Data Stream B would be 'Update Attributes and Hierarchies'. This setting will ensure that the hierarchy and attributes from the parent data stream (C) are updated based on the child data stream (B) without overwriting the measurement data that the parent is the source of truth for.
The 'Override Media Buy Hierarchy' option checked indicates that the hierarchy of the parent is to be considered as the main one, but the attributes and hierarchy can still be updated from the child data stream, which aligns with option B.
NEW QUESTION # 35
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?
Answer: B
Explanation:
The EXTRACT function is used to split a string based on a delimiter and return the segment at the specified position. The campaign names are structured with the segment of interest followed by an '@' sign. Therefore, the formula needs to extract the segment before the '@'.
The correct formula is: EXTRACT(csv['campaign_name']; '@', 1). This will take the 'campaign_name' field, split it at the '@' sign, and return the first segment (position 1), which is the three-letter code that is required. The other options are incorrect because they do not properly specify the delimiter and the segment position in the way needed to achieve the desired outcome.
NEW QUESTION # 36
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?
Answer: B
Explanation:
To create a linkage between Google Analytics and Salesforce Marketing Cloud data based on the "Message Send Key" and a portion of the "web_site_source_key," both values need to be harmonized into a common key. This is done by mapping the full Message Send Key from Salesforce Marketing Cloud and the extracted part of the web_site_source_key from Google Analytics to the same Custom Classification Key. This mapping will create a common identifier that can be used to combine the data from both sources for analysis and visualization.
NEW QUESTION # 37
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?
Answer: D
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. Reference: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.
NEW QUESTION # 38
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: B,D
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 # 39
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