Salesforce Marketing-Cloud-Intelligence Examcollection Questions Answers & New Marketing-Cloud-Intelligence Exam Preparation
Salesforce Marketing-Cloud-Intelligence Examcollection Questions Answers & New Marketing-Cloud-Intelligence Exam Preparation
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Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:
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Salesforce Marketing Cloud Intelligence Accredited Professional Exam Sample Questions (Q42-Q47):
NEW QUESTION # 42
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?
- A. Interest & Registered
- B. Registered
- C. Interest
- D. Confirmed Interest
- E. Confirmed Interest & Registered
Answer: A
Explanation:
Analyzing the Opportunity file with a filter set from January 7th to 11th, Opportunity Key '123AA01' appears under 'Interest' on January 6th and 8th, and under 'Registered' on January 10th. Therefore, during the specified date range, Opportunity Key '123AA01' is associated withboth 'Interest' and 'Registered' stages. Salesforce Marketing Cloud Intelligence provides the capability to map and track opportunity stages over time, allowing for historical stage tracking and reporting. This answer aligns with the ability to use pivot tables to filter and display data by specific attributes and timeframes, as outlined in the Salesforce Marketing Cloud Intelligence documentation.
NEW QUESTION # 43
Which option will yield the desiredresult:?
- A. Option 4
- B. Option 3
- C. Option 1
- D. Option 2
Answer: A
Explanation:
Option 4 presents two calculated measurements for 'Group Min Cost' with 'MIN' and 'AVG' aggregations. This approach aligns with the client's need for the minimum and average media cost values. 'Group Min Cost 4 MIN' will calculate the minimum media cost across the 'Media Buy Key', while 'Group Min Cost 4 FINAL' will average these minimum costs at the 'CampaignKey' level. This will yield the desired result where minimum costs are calculated at the Media Buy Key level and then averaged at the Campaign Key level.
NEW QUESTION # 44
A client wants to integrate their data within Marketing Cloud Intelligence to optimize their marketing insights and cross-channel marketing activityanalysis. 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?
- A. Scalability
- B. Use of code
- C. Ease of Maintenance
- D. Performance (Performance when loading a dashboard page)
- E. Processing (processing time when loading relevant data streams)
Answer: A,C,D
Explanation:
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.
NEW QUESTION # 45
Which three entities and/or functions can be used in an expression when building a calculated dimension?
- A. Mapped dimensions
- B. Calculated dimensions
- C. Mapped measurements
- D. The VLOOKUP function
- E. The EXTRACT function
Answer: A,C,E
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 # 46
What areunstable measurements?
- A. Measurements that are set with the LIFETIME aggregation function
- B. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'Not Empty'.
- C. Measurements for which Aggregation Settings are set as 'Auto' and Granularity is set as 'None'.
- D. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'None'.
Answer: D
Explanation:
Unstable measurements refer to metrics that are not aggregated in a standard manner across different grains of data, which can result in inconsistent or unpredictable results when reporting across different dimensions or time frames.
* Option C describes a scenario where measurements have manual (Not Auto) aggregation settings, meaning they do not automatically adjust to theaggregation level of the report. Combined with a Granularity setting of 'None', this can lead to instability because the metric isn't bound to a specific granularity, which can cause data inconsistencies or misinterpretations when analyzed at varying levels of detail.
NEW QUESTION # 47
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