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| Section | Weight | Objectives |
|---|---|---|
| Performance and Troubleshooting | 5% | - Monitoring and optimization - Common issues and fixes |
| Data Harmonization | 3% | - Pros and cons of harmonization approaches - Standardization methods - Cross-source alignment |
| Overarching Entities | 3% | - Use cases and application scenarios - Structure and configuration |
| Data Permissions and Governance | 5% | - User access and roles - Parent-child data setups - Data update controls |
| Datasets and Data Views | 10% | - Dataset creation and management - View configuration and sharing - Filtering and segmentation |
| General Functionalities | 13% | - Navigation and interface - Basic concepts and terminology - Core platform capabilities |
| Data Quality and Cleansing | 7% | - Duplicate management - Error handling and correction - Validation rules |
| Calculated Fields and Functions | 5% | - Extract and transformation formulas - VLOOKUP and FUZZYVLOOKUP - Custom calculations |
| Data Integration and Ingestion | 12% | - API and file-based import - Data streams and connectors - Refresh schedules and data flow |
| Dashboards and Visualization | 15% | - Chart types and usage - KPI and metric display - Dashboard design and layout |
| Data Mapping and Modeling | 5% | - Data structure and relationships - Source of truth configuration - Dimensions and measures |
| Automation and Workflow | 5% | - Automated distribution - Workflow setup |
| Reporting and Analytics | 12% | - Report creation and scheduling - Attribution models - Insight generation and interpretation |
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NEW QUESTION # 60
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
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan 7th -11th.Which option reflects the stage(s) the opportunity key 123AA01 is associated with?
Answer: C
Explanation:
Filtering the pivot table on January 7th-11th, we see that the Opportunity Key 123AA01 appears on January 6th with the stage 'Interest' and then on January 10th with the stage 'Registered'. Even though the 'Interest' stage is not within the filtered dates, it is the initial stage of the opportunity, so it should be counted along with the 'Registered' stage which falls within the filter range.
NEW QUESTION # 61
Client has provided sample flies 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.
As a first step, a Parent-Child relationship was created between the two files, and the following mapping was performed, within both data streams:
Please note:
* All other measurements were mapped as well to the appropriate fields.
* No other mapping manipulations or formulas were implemented.
How many records will the merged table hold?
Answer: B
Explanation:
Since the data sources are linked by shared Media Buy names and all other measurements are mapped to appropriate fields without additional manipulations, each unique Media Buy Name from Google DV360 will pair with its corresponding Media Buy Name in Google Campaign Manager. The number of records in the merged table will equal the number of unique Media Buy Names in Google DV360, provided there is a matching name in Google Campaign Manager. The sample shows 4 unique Media Buy Names in Google DV360, thus resulting in 4 records.
NEW QUESTION # 62
A client would like to integrate the following two sources:
Google Campaign Manager:
IAS:
After configuring a Parent-Child relationship between the files, which query should an implementation engineer run in order to QA the setup?
Answer: A
Explanation:
To QA the Parent-Child relationship setup between Google Campaign Manager and IAS data sources, it is essential to query fields that are common to both sources and that are relevant to the relationship. 'Media Buy Type' and 'Media Buy Name' are common identifiers between the two datasets. 'Impressions' from the Google Campaign Manager and 'Analyzed Impressions' from the IAS data are the metrics that should be compared to ensure they match or correlate as expected due to the Parent-Child relationship. The QA process involves checking that the data is correctly aligned and that the metrics from the parent source (Google Campaign Manager) are properly related to the metrics from the child source (IAS). Reference: Salesforce Marketing Cloud Intelligence documentation on data integration, Parent-Child relationships, and QA procedures for data setup.
NEW QUESTION # 63
Your client provided the following sources:
Source 1:
Source 2:
Source 3:
As can be seen, the Product values present in sources 2 and 3 are similar and can be linked with the first extraction from 'Media Buy Name' in source1 The end goal is to achieve a final view of Product Group alongside Clicks and Sign Ups, as described below:
Which two options will meet the client's requirement and enable the desired view?
Answer: A,C
Explanation:
To achieve a final view of Product Group alongside Clicks and Sign Ups, we should use:
Option A:
Custom Classification: By using a Custom Classification key populated with the extraction of the Media Buy Name in Source 1, we can then map 'Product' in Source 2 to this key and 'Product Group' to a Custom Classification level. This will allow for grouping and analysis by Product Group, as well as enable the desired view to be created.
Option D:
Harmonization Center: With patterns from Sources 1 and 3, we can create a harmonized dimension 'Product'. Then, by applying a Data Classification rule using Source 2, we can enhance the harmonized dimension. This allows us to align 'Product Group' with the 'Product' from Sources 1 and 3, facilitating an integrated view of Clicks and Sign Ups by Product Group.
NEW QUESTION # 64
A client provides the following three files:
File A:
File B:
File C:
File A was uploaded using the Ads data stream type.
The client would like to create this view (data from Files B & C) in Datorama:
Which proposed solution would cause a false connection between the two files?
Answer: B
Explanation:
With File A uploaded using the Ads data stream type, the client wishes to create a view incorporating data from Files B & C.
A false connection would occur if VLOOKUP in Data Stream B is used incorrectly to return "Day" and "Installs". In this scenario, VLOOKUP might inaccurately link data based on MB Name between File B and File A or File C, which do not have a "Day" field to correctly join on. Moreover, "Installs" data in File B doesn't exist, so VLOOKUP cannot correctly return this information. The correct method would be to use the "Media Buy New Name" to link File B and File C since they both have this field, ensuring accurate connection and avoiding data mismatches or false connections.
NEW QUESTION # 65
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