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| Section | Objectives |
|---|---|
| Topic 1: Data Quality and Troubleshooting | - Data validation and monitoring - Error handling and debugging - Performance optimization |
| Topic 2: Administration and Security | - Platform configuration - User roles and permissions - Data access control |
| Topic 3: Data Ingestion and Integration | - Data ingestion processes - Data sources and connectors - Data synchronization and scheduling |
| Topic 4: Data Modeling and Transformation | - Metrics and dimensions configuration - Data model structure and schema design - Normalization and harmonization |
| Topic 5: Analytics and Dashboarding | - Visualization best practices - Dashboard creation and configuration - Insights and reporting |
| Topic 6: Marketing Use Cases and Activation | - Cross-channel marketing insights - Business reporting use cases - Campaign performance analysis |
Wenn Sie finden, dass unsere AP-215 Prüfungsmaterialien Qualitätsproblem hat oder wenn Sie die Prüfung nicht bestanden haben, zahlen wir Ihnen bedingungslos die gesammte Summe zurück. Die Fragen und Antworten zur Salesforce AP-215 Zertifizierungsprüfung von It-Pruefung umfassen fast alle Wissensgebiete der Salesforce AP-215 Zertifizierungsprüfung.
32. 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.
33. Frage
A client provides the following two data streams:
Data Stream 1:
The client would like to use a VLOOKUP formula to calculate the Cost per Campaign Advertiser on January 1st 2020.
Which mapping options should the client apply to obtain the expected result?
Antwort: A
Begründung:
To calculate Cost per Campaign Advertiser using a VLOOKUP formula, the client needs to look up the 'Cost' from Data Stream 2 based on a matching 'Media Buy Name' in Data Stream 1. Option A shows that 'Media Buy Name' is the lookup value, which is correct. The 'Campaign Advertiser' is then linked to the 'Cost' from Data Stream 2 through the VLOOKUP formula applied to the 'Media Buy Custom Attribute 01' in Data Stream 2. This setup will correctly associate the cost with the campaign advertiser.
34. Frage
A client created a new KPI: CPS (Cost per Sign-up).
The new KIP is mapped within the data stream mapping, and is populated with the following logic: (Media Cost) / Sign-ups) As can be seen in the table below, CPS was created twice and was set with two different aggregations:
From looking at the table, what are the aggregation settings for each one of the newly created KPIs?
Antwort: B
Begründung:
The KPI CPS (Cost per Sign-up) would be calculated by dividing the 'Media Cost' by 'Sign-ups'. The table indicates that CPS is set with two different aggregations. In option C, CPS #1 is set to 'AUTO', which allows the system to decide the best aggregation method based on the context. CPS #2 is set to 'SUM', which indicates that the individual costs per sign-up are summed up across multiple records to provide a total cost per sign-up.
35. 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.
When harmonizing the Objective field from within the data stream mapping, which advantage is gained?
Antwort: A
Begründung:
By harmonizing the Objective field within data stream mapping, an organization can benefit from:
Ease of Maintenance: Harmonization allows for consistent naming conventions across different data sources and streams. This means when business logic or naming conventions change, updates can be made in one place and consistently applied across all data streams. It also reduces the complexity of managing multiple streams and ensures data consistency, which is vital for accurate reporting and analysis.
36. Frage
Your client has provided sample files of their data from the following data sources:
Google Campaign Manager
Below are the requirements from the client and additional information:
* The sources are linked to each other by shared Media Buy names.
* In addition to the mutual Media Buys, the sources contain campaign and site values. However, the client would like to see the campaign/site values coming from Google CM and not from Google DV360.
* The source of truth for cost is Google DV360
Which action(s) are needed to take place in order to meet the client's requirement and set Google DV360 as the source of truth for Cost?
Antwort: B
Begründung:
To set Google DV360 as the source of truth for cost:
The cost data from Google DV360 should be prioritized, which means ensuring that the 'Cost' field in Google Campaign Manager is not mapped or is mapped with less priority compared to Google DV360.
Given that DV360 is to be the source of truth, you do not want competing cost data from Campaign Manager. Unmapping 'Cost' in Google Campaign Manager prevents conflicting data between the two sources and upholds the integrity of the cost data coming from Google DV360.
37. Frage
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