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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:
Topic
Details
Topic 1
- Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 2
- Mapping: Marketing professionals will focus on Marketing Cloud Intelligence ingestion capabilities, assessing knowledge of data mapping processes and outcomes critical to efficient data organization.
Topic 3
- Harmonization Center (Patterns
- Data Classification
- Validation): Salesforce marketing professionals will learn about the Harmonization Center’s capabilities, including classification rules, validation lists, patterns, and harmonized dimensions to ensure data reliability.
Topic 4
- Data Integration Code Ability: This section evaluates proficiency with common Marketing Cloud Intelligence functions, enabling Salesforce marketing professionals to integrate diverse data sources effectively for comprehensive marketing intelligence.
Topic 5
- General Functionalities: In this topic, Salesforce marketing professionals will explore core functionalities of Marketing Cloud Intelligence. It measures understanding of platform features critical to data-driven marketing strategies and insights.
Topic 6
- Overarching Entities: Salesforce marketing professionals will deepen their understanding of overarching entities, their use cases, and application, crucial for strategic data organization and analysis.
Topic 7
- Harmonization Best Practices: Salesforce marketing professionals will analyze harmonization methods, properties, and their advantages and disadvantages, enhancing skills for optimizing data consistency across platforms.
Topic 8
- Calculated Dimensions & Measurements: This section measures skills in using calculated objects, recognizing aggregation types, and employing these tools for tailored marketing analytics.
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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q44-Q49):
NEW QUESTION # 44
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 meet the requirements?
- A. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.
- B. Create a second Pattern for Media Buy Name
- C. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension
- D. 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.
Answer: D
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.
NEW QUESTION # 45
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 add a validation list (with the two values) for the final Harmonized Dimension.
- B. Create a second Pattern for Media Buy Name
- C. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension
- D. 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.
Answer: D
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.
NEW QUESTION # 46
Which three entities and/or functions can be used in an expression when building a calculated dimension?
- A. The EXTRACT function
- B. The VLOOKUP function
- C. Mapped measurements
- D. Mapped dimensions
- E. Calculated dimensions
Answer: A,C,D
Explanation:
In the context of Marketing Cloud Intelligence, when building a calculated dimension, you can typically use:
* B. Mapped dimensions: These are dimensions that have been brought into Marketing Cloud Intelligence through the data integration process and have been mapped to a known schema or model.
* C. The EXTRACT function: This function can be used to dynamically create dimensions by extracting values from a mapped dimension or measurement.
* E. Mapped measurements: Similar to mapped dimensions, these are quantitative data points that have been integrated into the platform and can be referenced in calculations.
Calculated dimensions (D) and the VLOOKUP function (A) are not typically used within the expression for a calculated dimension. Calculated dimensions are usually an output, not an input, and VLOOKUP is a function typically used to enrich or connect data, not within the definition of a calculated dimension itself.
NEW QUESTION # 47
What is the relationship between "Media Buy Key" and "Creative Key?
- A. Many-to-many
- B. One-to-one
- C. Many-to-one (one Creative Key has many Media Buy Keys)
- D. One-to-many (one Media Buy ley has many Creative Key)
Answer: D
Explanation:
In Marketing Cloud Intelligence, the "Media Buy Key" is typically associated with the purchase details of a media campaign, such as the platform, audience, and budget. The "Creative Key" relates to the specific creative asset used within a campaign, like an image, video, or text. A single media buy can have multiple creative variations to test performance or to target different audiences, leading to a one-to-many relationship.
NEW QUESTION # 48
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?
- A. 0
- B. 1
- C. 2
- D. Depends on the Data Updates Permissions
Answer: A
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 # 49
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