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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reporting and Analytics | 12% | - Report creation and scheduling - Attribution models - Insight generation and interpretation |
| Topic 2: Automation and Workflow | 5% | - Workflow setup - Automated distribution |
| Topic 3: Data Integration and Ingestion | 12% | - API and file-based import - Data streams and connectors - Refresh schedules and data flow |
| Topic 4: Data Harmonization | 3% | - Standardization methods - Pros and cons of harmonization approaches - Cross-source alignment |
| Topic 5: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 6: Data Quality and Cleansing | 7% | - Error handling and correction - Duplicate management - Validation rules |
| Topic 7: Data Mapping and Modeling | 5% | - Dimensions and measures - Data structure and relationships - Source of truth configuration |
| Topic 8: Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
| Topic 9: Datasets and Data Views | 10% | - Dataset creation and management - View configuration and sharing - Filtering and segmentation |
| Topic 10: Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Topic 11: Calculated Fields and Functions | 5% | - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP - Custom calculations |
| Topic 12: General Functionalities | 13% | - Navigation and interface - Basic concepts and terminology - Core platform capabilities |
| Topic 13: Dashboards and Visualization | 15% | - KPI and metric display - Dashboard design and layout - Chart types and usage |
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NEW QUESTION # 59
Your client would like to create a new harmonization field - Exam Topic.
The below table represents the harmonization logic from each source.
As can be seen from the table there are in fact two fields that hold a certain connection: Exam ID and Exam Topic. The connection indicates that where an Exam ID is found -a single Exam Topic value is associated with it.
The Client has a requirement to be able to view measurements from all data sources sliced by Exam Topic values as seen in the following example:
Which harmonization feature should an Implementation engineer use to meet the client's requirement?
Answer: A
Explanation:
To meet the client's requirement of slicing measurements by 'Exam Topic' values, an Implementation Engineer should use Custom Classification. This feature allows different Exam IDs to be classified into their respective Exam Topics, ensuring that data from all sources can be accurately harmonized and analyzed based on these topics.
NEW QUESTION # 60
Your client is interested in ingesting the below file:
The client decided to upload the file to a new generic data stream type and map 'Date' to 'Day' and 'Number of Topics' to a generic custom metric.
In regards to the fields 'Meeting Code' and 'Meeting Name', your client is debating several options.
Which two options would you recommend in order to avoid data loss?
Answer: B,E
Explanation:
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
Explanation:
To avoid data loss and ensure each meeting is uniquely identified and its details are preserved, two mappings are recommended:
Option A:
'Meeting Code' should be mapped to the 'Main Generic Entity Key' to uniquely identify each meeting.
'Meeting Name' should be mapped to a 'Main Generic Entity custom attribute' to store additional information about the meeting.
Option E:
Concatenation of 'Meeting Code' and 'Meeting Name' should be mapped to 'Main Generic Entity Key'. This ensures a unique identifier for each meeting is created combining both pieces of information, preventing any mix-ups between meetings with similar codes or names.
Additionally, mapping 'Meeting Code' and 'Meeting Name' to their respective 'Main Generic Entity Attribute' fields will allow for more detailed filtering and reporting capabilities within Marketing Cloud Intelligence.
NEW QUESTION # 61
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: A
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 # 62
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.
The client would like to have a "Site Revenue" measurement.
This measurement should return the highest revenue value per Site, for example:
For Site Key 'SK_C_2', the "Site Revenue" should be $7.00.
When aggregated by date, the "Site Revenue" measurement should return the total sum of the results of all sites.
For example:
For the date 1 Apr 2020, "Site Revenue" should be $11.00 (sum of Site Revenue for Site Keys 'SK_C_1' ($4.00) and 'SK_C_2' ($7.00))
Which options will yield the desired result;
Answer: A
Explanation:
Option #2: It suggests using the 'SUM' function to aggregate the 'Site Revenue' for each 'Site Key'. This is necessary to ensure that when aggregated by date, 'Site Revenue' should return the total sum of the highest revenue for all sites.
Option #4: It indicates changing the Aggregation Function of Revenue to 'MAX' within Data Stream C.
This ensures that for a given 'Site Key', the highest revenue value is selected, which is correct for individual site revenue determination.
Combining Option #2 and Option #4 will provide the desired result:
For an individual 'Site Key', it will give the highest revenue (using MAX aggregation in Option #4).
When aggregating by date across all 'Site Key's, it will sum the highest revenues (using the SUM function in Option #2).
NEW QUESTION # 63
A client's data consists of three data streams as follows:
Data Stream A:
Answer: B
Explanation:
For the client's data consisting of three data streams, setting Data Stream A as the Parent allows for inheriting attributes and hierarchies from it to the child data streams. This ensures consistency across the data streams, making it possible to analyze the data collectively, using the structure and attributes defined in the Parent data stream.
NEW QUESTION # 64
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