Question # 1
Your client provided the following sources:
Source 1:
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? | A. Custom Classification: 1
Source 1: Custom Classification key will be populated with the extraction of the Media Buy Name.
Source 2: ‘Product’ will be mapped to Custom Classification key and ‘Product Group’ to a Custom Classification level. Exam Timer
Source 3: ‘Product will be mapped to Custom Classification key. Came | B. Overarching Entities:
Source 1: custom classification key will be populated with the extraction of the Media Buy Name.
Source 2: ‘Product’ will be mapped to Product field and ‘Product Group’ to Product Name.
Source 3: ‘Product’ will be mapped to Product field. | C. Parent Child:
All sources will be uploaded to the same data stream type - Ads. The setup is the following:
Source 1: Media Buy Key —- Media Buy Key, extracted product value — Media Buy Attribute.
Source 2: Product — Media Buy Key, Product Group —- Media Buy Attribute.
Source 3: Product — Media Buy Key. | D. Harmonization Center:
Patterns from sources 1 and 3 generate harmonized dimension ‘Product’. Data Classification rule, using source 2, is applied on top of the harmonized dimension |
A. Custom Classification: 1
Source 1: Custom Classification key will be populated with the extraction of the Media Buy Name.
Source 2: ‘Product’ will be mapped to Custom Classification key and ‘Product Group’ to a Custom Classification level. Exam Timer
Source 3: ‘Product will be mapped to Custom Classification key. Came
D. Harmonization Center:
Patterns from sources 1 and 3 generate harmonized dimension ‘Product’. Data Classification rule, using source 2, is applied on top of the harmonized dimension
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.
Question # 2
| Which two statements are correct regarding the Parent-Child configuration? | | A. Parent-Child configurations can cause performances issues | | B. A Parent-Child cannot be configured between an Ads data stream type and a Conversion Tag one. | | C. Parent-Child links different tables based on shared key values | | D. Parent-Child allows sharing both dimensions and measurements |
A. Parent-Child configurations can cause performances issues
C. Parent-Child links different tables based on shared key values
Explanation: Parent-Child configurations in Marketing Cloud Intelligence are used to link different data tables based on shared key values, allowing for the relational organization of data across various streams. While this setup enhances data analysis and reporting by maintaining logical relationships between parent and child tables, it can also introduce performance issues. The complexity increases with the number of relationships and the volume of data, potentially slowing down query processing and data manipulation.
Additionally, Parent-Child configurations facilitate the sharing of dimensions and measurements across linked tables, enhancing the data's usability without duplicating it.
Question # 3
A client’s data consists of three data streams as follows:
Data Stream A:
* The data streams should be linked together through a parent-child relationship.
* Out of the three data streams, Data Stream C is considered the source of truth for both the dimensions and measurements.
Assuming the data was ingested properly and the Parent Child was created correctly according to the client's requirements, what is the total Impressions value for Campaign Key ‘CK_3’? | | A. N-A | | B. 100 | | C. 300 | | D. 150 |
B. 100
Explanation: Assuming that Data Stream A is set correctly with parent-child relationships: To find the total impressions for Campaign Key 'CK_3', you would look in Data Stream A, since it contains the 'Impressions' metric. As per the provided data, Campaign Key ‘CK_3’ has 100 impressions.
Question # 4
An implementation engineer is requested to create the harmonization field - Magician
This field should come from multiple Twitter Ads data streams, and should follow the below logic:
Using the Harmonization Center, the engineer created a single Pattern for Campaign Name. What other action should the engineer take to meetthe requirements? | | A. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for ‘Messi’ and another for Ronaldo’) for the final Harmonized Dimension. | | B. Create a second Pattern for Media Buy Name | | C. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension. | | D. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension |
A. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for ‘Messi’ and another for Ronaldo’) for the final Harmonized Dimension.
Explanation: For the field 'Magician', the engineer is required to follow a logic that extracts a value from 'Campaign Name' and checks against a validation list for specific values ('Messi' or 'Ronaldo'). If those values are not found, it should instead extract from 'Media Buy Name'. To accomplish this, the engineer should:
Use the created Pattern for 'Campaign Name'.
Create a second Pattern for 'Media Buy Name' to capture the fallback values. Apply two Classification Rules to the Harmonized Dimension: one for the value 'Messi' and another for 'Ronaldo'. This is to check the extracted 'Campaign Name' against these specific values.
These steps ensure that the 'Magician' field will be populated with the correct values from the respective data streams following the specified logic.
Question # 5
A client has integrated data from Facebook Ads. Twitter ads, and Google ads in marketing Cloud intelligence. For each data source, the source, the data follows a naming convensions as …
Facebook Ads Naming Convention - Campaign Name:
CampID_CampName#Market_Object#object#targetAge_TargetGender Twitter Ads Naming Convention- Media Buy Name MarketTargeAgeObjectiveOrderID
Google ads Naming Convention-Media Buy Name: Buying_type_Market_Objective
The client wants to harmonize their data on the common fields between these two platforms (i.e. Market and Objective) using the Harmonization Center. Given the above information, which statement is correct regarding the ability to implement this request?
wet Me - Given the above information, which statement i 's Correct regarding the ability to implement this request? | | A. The clientWi-Fibe able to harmonize only Google Ads and Twitter Ads, as Facebook Ads naming convention contains mufti delimiters. | | B. it is not possible to do this, as the naming conventions are different | | C. This is not possible as the naming conventions are in different fields (Campaign Name and Placement Name) | | D. The client will be able to do this and it will require building three patterns. |
D. The client will be able to do this and it will require building three patterns.
Explanation: Despite the different naming conventions, harmonization is possible using patterns in the Harmonization Center. By extracting the 'Market' and 'Objective' components from the naming conventions of each platform, three separate patterns would be created to map these common fields consistently across the data from Facebook Ads, Twitter Ads, and Google Ads.
Question # 6
The following file was uploaded into Marketing Cloud Intelligence as a generic dataset type:
The mapping is as follows:
Day — Day
Web_site_source — Main Generic Entity Attribute 01 Page Views — Generic Metric 1
*Note that ‘web_site_key’ and ‘web_site_name’ are NOT mapped.
How many rows will be stored in Marketing Cloud Intelligence after the above file is ingested? | | A. 4 | | B. 0 | | C. 1 | | D. 2 |
A. 4
Explanation: In Marketing Cloud Intelligence, when a file is uploaded as a generic dataset type and mapped accordingly, each unique combination of the mapped fields results in a separate row in the database. The file in question has been mapped with 'Day' to 'Day', 'Web_site_source' to 'Main Generic Entity Attribute 01', and 'Page Views' to 'Generic Metric 1'. The 'web_site_key' and 'web_site_name' are not mapped and thus, won't affect the row count.
Since there are 4 unique combinations of the mapped fields in the uploaded file (each day and source combination is unique), Marketing Cloud Intelligence will store 4 rows after ingestion, corresponding to each unique combination of 'Day' and 'Web_site_source'.
Question # 7
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.
Which three advantages does a client gain from using Calculated Dimensions as the harmonization method for creating the Objective field? | | A. Scalability - future data streams that will follow similar logic will be automatically harmonized. | | B. Data model restrictions - Calculated Dimensions do not need to adhere to Marketing Cloud Intelligence's data model | | C. Processing - creation of Calculated Dimensions will ease the processing time of the data streams it relates to | | D. Ease of Maintenance - the logic is written and populated in one centralized place | | E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database. |
A. Scalability - future data streams that will follow similar logic will be automatically harmonized.
D. Ease of Maintenance - the logic is written and populated in one centralized place
E. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database.
Explanation: Scalability: Using Calculated Dimensions allows the client to apply the same harmonization logic to future data streams, ensuring consistency and reducing the need for individual adjustments.
Ease of Maintenance: With the logic centralized in Calculated Dimensions, any adjustments or updates are applied in one place, simplifying ongoing management.
Performance: Calculated Dimensions can improve dashboard performance because their values are pre-computed and stored, reducing the need for real-time calculations when loading dashboards.
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