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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Permissions and Governance | 5% | - User access and roles - Data update controls - Parent-child data setups |
| Topic 2: Datasets and Data Views | 10% | - View configuration and sharing - Filtering and segmentation - Dataset creation and management |
| Topic 3: Calculated Fields and Functions | 5% | - Custom calculations - VLOOKUP and FUZZYVLOOKUP - Extract and transformation formulas |
| Topic 4: Data Mapping and Modeling | 5% | - Dimensions and measures - Source of truth configuration - Data structure and relationships |
| Topic 5: Data Integration and Ingestion | 12% | - API and file-based import - Refresh schedules and data flow - Data streams and connectors |
| Topic 6: Dashboards and Visualization | 15% | - Dashboard design and layout - KPI and metric display - Chart types and usage |
| Topic 7: Data Harmonization | 3% | - Cross-source alignment - Standardization methods - Pros and cons of harmonization approaches |
| Topic 8: Data Quality and Cleansing | 7% | - Validation rules - Duplicate management - Error handling and correction |
| Topic 9: General Functionalities | 13% | - Navigation and interface - Basic concepts and terminology - Core platform capabilities |
| Topic 10: Reporting and Analytics | 12% | - Report creation and scheduling - Insight generation and interpretation - Attribution models |
| Topic 11: Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
| Topic 12: Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Topic 13: Automation and Workflow | 5% | - Automated distribution - Workflow setup |
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NEW QUESTION # 24
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
Answer: A,B
Explanation:
For performance issues when loading a dashboard using CRM data stream type:
Pacing can create performance issues because daily rows for every lead and opportunity key can result in a very large number of rows, increasing load times.
Having only calculated measurements means there are no direct, mappable values to query against, which can increase the computational load and affect performance.
NEW QUESTION # 25
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 # 26
A technical architect is provided with the logic and Opportunity file shown below:
The opportunity status logic is as follows:
For the opportunity stages "Interest", "Confirmed Interest" and "Registered", the status should be "Open".
For the opportunity stage "Closed", the opportunity status should be closed Otherwise, return null for the opportunity status
Given the above file and logic and assuming that the file is mapped in a GENERIC data stream type with the following mapping:
"Day" - Standard "Day" field
"Opportunity Key" > Main Generic Entity Key
"Opportunity Stage" - Generic Entity Key 2
"Opportunity Count" - Generic Custom Metric
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on January (entire month). What is the number of opportunities in the Interest stage?
Answer: A
Explanation:
Based on the Opportunity file, the Opportunity Stage of 'Interest' occurs 3 times across unique Opportunity Keys. Since the pivot table is filtered to present the entire month of January and the Opportunity Stage 'Interest' is listed three times with different Opportunity Keys, the count of opportunities in the 'Interest' stage would be 3.
NEW QUESTION # 27
What is the relationship between "Media Buy Key" and "Campaign Key"?
Answer: C
Explanation:
Typically, 'Campaign Key' is a unique identifier for a specific marketing campaign, while 'Media Buy Key' refers to the purchases of advertising space associated with that campaign. A campaign can have multiple media buys, so the relationship is many-to-one, with many media buys (Media Buy Keys) associated with a single campaign (Campaign Key).
NEW QUESTION # 28
What Is a disadvantage of using a Vlookup formula?
Answer: A
Explanation:
The use of VLOOKUP formulas can increase the processing time of data streams because it requires a lookup operation for each row in the data set. When large volumes of data are involved, or when multiple VLOOKUPs are used, this can significantly impact processing time due to the complexity and computational requirements of matching and retrieving the data.
NEW QUESTION # 29
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