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| Section | Weight | Objectives |
|---|---|---|
| Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
| Dashboards and Visualization | 15% | - Dashboard design and layout - KPI and metric display - Chart types and usage |
| Data Mapping and Modeling | 5% | - Dimensions and measures - Data structure and relationships - Source of truth configuration |
| Reporting and Analytics | 12% | - Attribution models - Report creation and scheduling - Insight generation and interpretation |
| Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| General Functionalities | 13% | - Core platform capabilities - Basic concepts and terminology - Navigation and interface |
| Data Quality and Cleansing | 7% | - Error handling and correction - Duplicate management - Validation rules |
| Data Harmonization | 3% | - Standardization methods - Pros and cons of harmonization approaches - Cross-source alignment |
| Datasets and Data Views | 10% | - Filtering and segmentation - Dataset creation and management - View configuration and sharing |
| Data Integration and Ingestion | 12% | - Data streams and connectors - API and file-based import - Refresh schedules and data flow |
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Calculated Fields and Functions | 5% | - Custom calculations - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP |
| Overarching Entities | 3% | - Structure and configuration - Use cases and application scenarios |
多くの人がAP-215試験を非常に重視する必要があります。また、試験に合格することは多くの人にとって簡単なことではないこともわかっています。そのため、多くの人にとって優れた学習方法は非常に重要です。さらに、適切な学習ツールも同様に重要です。AP-215リファレンスガイドは、リラックスした状態で試験に合格するのに役立ちます。弊社からAP-215認定試験ガイド資料をご紹介します。 AP-215学習教材は、AP-215試験に合格するのに非常に有用で役立つと考えています。
質問 # 31
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?
正解：A
解説：
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.
質問 # 32
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?
正解：A、E
解説：
'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.
質問 # 33
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" - Main Generic Entity Attribute
"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 11th. What is the number of opportunities in the Interest stage?
正解：A
解説：
Since the pivot table is filtered on January 11th and the provided Opportunity file does not show any records dated January 11th, there are zero opportunities in the Interest stage for that date. Salesforce Marketing Cloud Intelligence allows users to create pivot tables and filter data based on specific criteria, such as dates. In this case, the filter would exclude all rows that do not match the specified date, resulting in a count of zero for the Interest stage. This would apply to any stage since there are no records for January 11th. Reference can be made to Salesforce Marketing Cloud Intelligence documentation on filtering and pivot tables.
質問 # 34
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：A
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 # 35
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?
正解：A、C、E
解説：
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.
質問 # 36
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