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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 2
- Vlookup: This section evaluates proficiency of marketing professionals in Vlookup statements and their properties, ensuring accurate data referencing and streamlined data manipulation for marketing intelligence tasks.
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
- QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 5
- CRM: This topic tests knowledge of CRM properties and their behavior within Marketing Cloud Intelligence. This knowledge is crucial for syncing customer relationship data with marketing campaigns.
Topic 6
- Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 7
- 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.
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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q47-Q52):
NEW QUESTION # 47
An Implementation engineer is requested to create a new harmonization field 'Offer' and apply the following logic:
The implementation engineer to use the Harmonization Center. Which of the below actions can help implement the new dimension 'Offer?
- A. Two separate patterns (filtered by Linkedln or AdRoll sources).
Another single pattern for Web Analytics Site Source (filtered by Google Analytics source), extracting all three positions A total of 3 patterns. - B. Two separate patterns (filtered by Linkedin or AdRoll sources).
Another single pattern for Campaign Name (filtered by Google Analytics source).
A total of 3 patterns. - C. Two separate patterns (filtered by Linkedin or AdRoll sources)
Within Google Analytics' mapping A formula that reflects the logic above will be populated within a Web Analytics Site custom attribute Another pattern to be created for the newly Web Analytics Site custom attribute (filtered by Google Analytics source).
A total of 3 patterns. - D. Two separate patterns (filtered by Linkedin or AdRoll sources)
Within Google Analytics' mapping: A formula that reflects the logic above will be populated within a Campaign custom attribute.
Another pattern to be created for the newly campaign attribute (filtered by Google Analytics source).
A total of 3 patterns
Answer: D
Explanation:
To implement the new harmonization field 'Offer', the implementation engineer would create two separate harmonization patterns for LinkedIn and AdRoll sources, extracting the 'Campaign Name' using the specified delimiter and position. Then, within Google Analytics' mapping, a custom attribute for the 'Campaign' would be created to apply the formula logic based on the source. This allows for the harmonization of campaign data across different platforms, ensuring consistency in the reporting and analysis within Marketing Cloud Intelligence. The total patterns required would be three, one for each data source involved.
NEW QUESTION # 48
Which Marketing Cloud Intelligence field is considered an attribute and not a "variable"?
- A. Geo Location
- B. Campaign Category
- C. Device Browser
- D. Device Category
Answer: D
Explanation:
In Marketing Cloud Intelligence, attributes refer to characteristics of the data that describe the environment or context but do not change within the scope of the data being analyzed. 'Device Category' is typically an attribute as it describes a characteristic of the device used and doesn't vary within a given session or user interaction. In contrast, variables are typically metrics or dimensions that can change value or be measured.
NEW QUESTION # 49
An implementation engineer has been asked to perform QA for a standard file ingestion, done by the client.
The source file that was ingested can be seen below:
The number of rows added to this data stream is 3. What could have led to this discrepancy?
- A. All fields are mapped except for the Media Buy Name.
- B. All fields are mapped except for the Media Buy Key.
- C. All fields are mapped except for the Creative Name
- D. All fields are mapped except for the Campaign Key
Answer: D
Explanation:
The source file shows data related to media buys, including a 'Media Buy Key', 'Media Buy Name', 'Campaign Key', and 'Site Key', among other fields. If only three rows were added, and the discrepancy is due to a missing field, it's likely that 'Campaign Key' is the field not mapped, because it is crucial for linking related records in the data stream. Without the 'Campaign Key', the system cannot associate the media buy data with specific campaigns, leading to a potential loss of data rows during ingestion.
NEW QUESTION # 50
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 dimensions
- D. Mapped measurements
- 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 # 51
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?
- A. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Main Generic Entity custom attribute'. - B. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
'Meeting Name' will be mapped to 'Generic Entity 2 Key'. - C. 'Meeting Code' will be mapped to 'Main Generic Entity custom attribute'.
'Meeting Name' will be mapped to 'Generic Entity Key' - D. 'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'. - E. Concatenation of both 'Meeting Code' and 'Meeting Name' will be mapped to 'Main Generic Entity Key'.
'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
Answer: A,E
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.
NEW QUESTION # 52
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