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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Integration and Ingestion | 12% | - API and file-based import - Refresh schedules and data flow - Data streams and connectors |
| Topic 2: Reporting and Analytics | 12% | - Insight generation and interpretation - Attribution models - Report creation and scheduling |
| Topic 3: Data Quality and Cleansing | 7% | - Validation rules - Error handling and correction - Duplicate management |
| Topic 4: Data Harmonization | 3% | - Cross-source alignment - Pros and cons of harmonization approaches - Standardization methods |
| Topic 5: Calculated Fields and Functions | 5% | - VLOOKUP and FUZZYVLOOKUP - Custom calculations - Extract and transformation formulas |
| Topic 6: Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Topic 7: Datasets and Data Views | 10% | - View configuration and sharing - Dataset creation and management - Filtering and segmentation |
| Topic 8: Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Topic 9: Data Permissions and Governance | 5% | - Parent-child data setups - User access and roles - Data update controls |
| Topic 10: Data Mapping and Modeling | 5% | - Data structure and relationships - Source of truth configuration - Dimensions and measures |
| Topic 11: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 12: Dashboards and Visualization | 15% | - KPI and metric display - Dashboard design and layout - Chart types and usage |
| Topic 13: General Functionalities | 13% | - Basic concepts and terminology - Navigation and interface - Core platform capabilities |
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NEW QUESTION # 25
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.
Which three advantages does a client gain from using Calculated Dimensions as the harmonization method for creating the Objective field?
Answer: A,B,D
Explanation:
Scalability: Using Calculated Dimensions allows the client to apply the same harmonization logic to future data streams, ensuring consistency and reducing the need for individual adjustments.
Ease of Maintenance: With the logic centralized in Calculated Dimensions, any adjustments or updates are applied in one place, simplifying ongoing management.
Performance: Calculated Dimensions can improve dashboard performance because their values are pre-computed and stored, reducing the need for real-time calculations when loading dashboards.
NEW QUESTION # 26
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: D
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 # 27
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:
The mapping is as follows:
Day - Day
web_site_key -> Main Generic Entity Key
web_site_name -> Main Generic Entity Name
Web_site_source -> Main Generic Entity Attribute 01
Page Views - Generic Metric 1
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested?
Answer: B
Explanation:
With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source'. As 'Day' is mapped to 'Day', 'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and 'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'. Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.
NEW QUESTION # 28
What is the relationship between "Media Buy Key" and "Creative Key?
Answer: B
Explanation:
In Marketing Cloud Intelligence, the "Media Buy Key" is typically associated with the purchase details of a media campaign, such as the platform, audience, and budget. The "Creative Key" relates to the specific creative asset used within a campaign, like an image, video, or text. A single media buy can have multiple creative variations to test performance or to target different audiences, leading to a one-to-many relationship.
NEW QUESTION # 29
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,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 # 30
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