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| Section | Weight | Objectives |
|---|---|---|
| Data Quality and Cleansing | 7% | - Validation rules - Error handling and correction - Duplicate management |
| Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| Calculated Fields and Functions | 5% | - Custom calculations - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP |
| General Functionalities | 13% | - Navigation and interface - Core platform capabilities - Basic concepts and terminology |
| Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| Data Permissions and Governance | 5% | - Data update controls - Parent-child data setups - User access and roles |
| Data Integration and Ingestion | 12% | - API and file-based import - Refresh schedules and data flow - Data streams and connectors |
| Reporting and Analytics | 12% | - Insight generation and interpretation - Report creation and scheduling - Attribution models |
| Data Mapping and Modeling | 5% | - Source of truth configuration - Dimensions and measures - Data structure and relationships |
| Datasets and Data Views | 10% | - Dataset creation and management - View configuration and sharing - Filtering and segmentation |
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
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43. Frage
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.
What three advantages are gained when using Patterns & Data Classification as the harmonization method for creating the Objective field?
Antwort: A,D,E
Begründung:
Patterns & Data Classification in Marketing Cloud Intelligence offer several advantages. These include:
Ease of Maintenance (A): Patterns allow for the standardization of data harmonization processes. Once set up, they can be easily maintained and adjusted as needed, without having to manipulate each data stream individually.
Performance (B): By using patterns, data is classified and standardized at ingestion, which can improve the performance of dashboard page loading because the system does not need to perform complex, on-the-fly calculations or transformations.
Scalability (D): Patterns can be applied across multiple data streams consistently, allowing them to scale with the data. This means that as the amount of data grows or as new data sources are added, the same patterns can be reused, ensuring that the data remains harmonized.
44. Frage
An implementation engineer has been asked by a client for assistance with the following problem:
The below dataset was ingested:
However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type' is 4.
What could be the reason for this discrepancy?
Antwort: A
Begründung:
The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is 8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. Reference: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.
45. Frage
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?
Antwort: C,D
Begründung:
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.
46. Frage
An implementation engineer has been provided with 4 different source files: 03m 16s
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 &
File structure/headers:
Category - links back to Campaign Category (Twitter Ads)
Which proposed solution meets the client's requirements for the above use case?
Antwort: D
Begründung:
For the given use case, where the Twitter Ads data stream needs to be enriched with classifications from three other sources, the correct implementation would involve creating links between the various fields across these files.
Option A is correct because it shows the correct usage of the fields from the classification files:
"Creative ID" in the Creative Classification file is linked to the "Creative Key" in the Twitter Ads data, allowing for enrichment with creative details.
"Placement ID" in the Placement Classification file is linked to a corresponding field in the Twitter Ads data, allowing for placement details to be added.
"Category" in the Campaign Category Classification file is linked back to "Campaign Category" in the Twitter Ads data, thus enriching the campaign data with the correct categories.
This configuration correctly uses VLOOKUP to enrich the Twitter Ads data stream with additional details from the classification files, aligning with best practices for data integration and enrichment in Marketing Cloud Intelligence.
47. Frage
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?
Antwort: B
Begründung:
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.
48. Frage
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