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我々の目標はAP-215試験に準備するあなたに試験に合格させることです。この目標を実現するようには、我が社のXhs1991は試験改革のとともにめざましく推進していき、最も専門的なAP-215問題集をリリースしています。現時点で我々のSalesforce AP-215問題集を使用しているあなたは試験にうまくパースできると信じられます。心配なく我々の真題を利用してください。
| Section | Weight | Objectives |
|---|---|---|
| Data Mapping and Modeling | 5% | - Source of truth configuration - Dimensions and measures - Data structure and relationships |
| Data Integration and Ingestion | 12% | - Data streams and connectors - Refresh schedules and data flow - API and file-based import |
| Data Quality and Cleansing | 7% | - Error handling and correction - Validation rules - Duplicate management |
| Calculated Fields and Functions | 5% | - Custom calculations - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP |
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
| Data Harmonization | 3% | - Standardization methods - Cross-source alignment - Pros and cons of harmonization approaches |
| Reporting and Analytics | 12% | - Insight generation and interpretation - Attribution models - Report creation and scheduling |
| Data Permissions and Governance | 5% | - User access and roles - Data update controls - Parent-child data setups |
| Datasets and Data Views | 10% | - Filtering and segmentation - View configuration and sharing - Dataset creation and management |
| Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| General Functionalities | 13% | - Basic concepts and terminology - Core platform capabilities - Navigation and interface |
| Performance and Troubleshooting | 5% | - Common issues and fixes - Monitoring and optimization |
最近多くの人はIT資格認定試験という悩みがあるようですが、実は、この時代では、Salesforce資格は難しくないです。我々Xhs1991はIT資格認定試験資料の販売者のリーダーとして、信頼できるAP-215問題集を提供します。躊躇われずに我々の模擬試験を利用してください。
質問 # 17
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?
正解：D
解説：
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.
質問 # 18
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 Jan 7th - 10th. How many different stages are presented in the table?
正解：D
解説：
Based on the Opportunity file and considering the filter dates from January 7th to 10th, the different stages presented are 'Interest', 'Confirmed Interest', and 'Registered'. This makes a total of 3 different stages that would be presented in the pivot table. Salesforce Marketing Cloud Intelligence allows for the creation of pivot tables that can display counts of entities across different dimensions, in this case, Opportunity Stages. Reference to Salesforce Marketing Cloud Intelligence documentation that covers data mapping and pivot table creation would support this conclusion.
質問 # 19
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?
正解：D
解説：
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.
質問 # 20
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?
正解：A
解説：
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.
質問 # 21
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?
正解：C
解説：
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.
質問 # 22
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