Fast2test Marketing-Cloud-Intelligenceリアル試験問題解答は更新された[2024年07月22日]
お手軽に合格させる 最新Salesforce Marketing-Cloud-Intelligence問題集には63問があります
質問 # 36
An implementation engineer has been asked by a client for assistance with the following problem:
The below dataset was ingested:
However, when performing QA and querying a pivot table with Campaign Category and Clicks, the value for Type' is 4.
What could be the reason for this discrepancy?
- A. The aggregation function is set as LIFETIME
- B. The measurement 'Clicks' is set as a percentage.
- C. A mapping formula was populated, indicating not to bring Type! values.
- D. The aggregation function is set as AVG
正解：D
解説：
The discrepancy of 'Clicks' being reported as 4 for 'Type1' when the sum of clicks in the dataset for 'Type1' is
8 (2 on 02/02/2021 and 6 on 03/02/2021) suggests that the aggregation function used in the pivot table is set to average (AVG) rather than sum. Salesforce Marketing Cloud Intelligence allows setting different aggregation functions for metrics, and setting it to average would result in such a discrepancy when more than one entry for the same type exists. References: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.
質問 # 37
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. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
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'.
質問 # 38
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?
- A. VLOOKUP in Data Stream C. Vlookup will return "MB Name"
- B. Custom classification
- C. Data Classification
- D. VLOOKUP in Data Stream B. Vlookup will return "Day" and "Installs"
正解：D
解説：
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, soVLOOKUP 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.
質問 # 39
What areunstable measurements?
- A. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'Not Empty'.
- B. Measurements for which Aggregation Settings are set as 'Auto' and Granularity is set as 'None'.
- C. Measurements that are set with the LIFETIME aggregation function
- D. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'None'.
正解：D
解説：
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.
質問 # 40
After uploading a standard file into Marketing Cloud intelligence via totalConnect, you noticed that the number of rows uploaded (to the specific data stream) is NOT equal to the number of rows present in the source file. What are two resource that may cause thisgap?
- A. All mapped Measurements for a given row have values equal to zero
- B. The source file does not contain the mediaBuy entity
- C. The file does not contain any measurements (dimension only)
- D. Main entity is not mapped
正解：A、D
解説：
In Marketing Cloud Intelligence, discrepancies between the number of rows uploaded and the number of rows present in the source file can be caused by several factors. If all mapped measurements for a row are zero, that row may be excluded from the upload, as it does not contribute to the analytics. Additionally, if the main entity, which acts as the primary identifier for records, is not mapped, the system cannot correctly ingest the data as it lacks the necessary reference to organize and store the information.
質問 # 41
An Implementation engineer is requested to create anew 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 Linkedin or AdRoll sources).
Another single pattern for Campaign Name (filtered by Google Analytics source).
A total of 3 patterns. - B. 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. - C. 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.
- 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
正解：D
解説：
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.
質問 # 42
Which two statements are correct regarding the Parent-Child configuration?
- A. Parent-Child links different tables based on shared key values
- B. A Parent-Child cannot be configured between an Ads data stream type and a Conversion Tag one.
- C. Parent-Child configurations can cause performances issues
- D. Parent-Child allows sharing both dimensions and measurements
正解：A、C
解説：
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 variousstreams. 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.
質問 # 43
Your client would like to create a new harmonization field - Exam Topic.
The below table represents the harmonization logic from each source.
As can be seen from the table, there are in fact two fields that hold a certain connection: Exam ID and Exam Topic. The connection indicates that where an Exam ID is found - a single Exam Topic value is associated with it.
The client has a requirement to be able to view measurements from all data sources sliced by Exam Topic values, as seen in the following example:
The client suggested to create, without any mapping manipulations, several patterns via the harmonization center that will generate two Harmonized Dimensions:
Exam ID
Exam Topic
Given the above information, which statement is correct regarding the ability to implement this request with the above suggestion?
- A. Only if 5 different Patterns are created, from 5 different fields - the solution will work.
- B. The Harmonized field for Exam ID is redundant. One Harmonized dimension for Exam Topic is enough for a sustainable and working solution
- C. The above Patterns setup will not work for this use case.
- D. The solution will work - the client will be able to view Exam Topic with Email Sends.
正解：B
解説：
If the harmonization logic consistently associates a single Exam Topic with each Exam ID across all data sources, then creating two harmonized dimensions may be unnecessary. One harmonized dimension for Exam Topic would suffice because it inherently carries the Exam ID's uniqueness within it. The harmonized dimension for Exam Topic would allow the client to slice the data by Exam Topic values, fulfilling the requirement.
質問 # 44
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. it is not possible to do this, as the naming conventions are different
- B. The clientWi-Fibe able to harmonize only Google Ads and Twitter Ads, as Facebook Ads naming convention contains mufti delimiters.
- 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.
正解：D
解説：
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.
質問 # 45
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
"Opportunity Count" - Generic Custom Metric
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on January (entire month). What is the number of opportunities in the Interest stage?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
Based on the Opportunity file, the Opportunity Stage of 'Interest' occurs 3 times across unique Opportunity Keys. Since the pivot table is filtered to present the entire month of January and theOpportunity Stage 'Interest' is listed three times with different Opportunity Keys, the count of opportunities in the 'Interest' stage would be
3.
質問 # 46
Which two statements are correct regarding variable Dimensions in marketing Cloud intelligence's data model?
- A. All variables exist in every data set type, hence are considered as overarching dimensions
- B. These dimensions are stored at the workspace level
- C. Variable Dimensions hold a Many-to-Many relationship with its main entity
- D. These are stand alone dimensions that pertain to the data set itself rather than to a specific entity
正解：B、C
解説：
Variable dimensions in Marketing Cloud Intelligence's data model are flexible and can be associated with multiple entities, forming a many-to-many relationship. These dimensions are configured and stored at the workspace level, allowing for customization and alignment with specific reporting needs and analytics practices.
質問 # 47
A client's data consists of three data streams as follows:
Data Stream A:
- A. Update Attributes
- B. Update Attributes and Hierarchies
- C. It doesn't matter. As long as Data stream A is set as a Parent', the rest of the Data Updates Permissions are irrelevant.
- D. Inherit Attributes and Hierarchies
正解：D
解説：
For the client's data consisting of three data streams, setting Data Stream A as the Parent allows for inheriting attributes and hierarchies from it to the child data streams. This ensuresconsistency across the data streams, making it possible to analyze the data collectively, using the structure and attributes defined in the Parent data stream.
質問 # 48
Which three entities and/or functions can be used in an expression when building a calculated dimension?
- A. Mapped measurements
- B. Calculated dimensions
- C. The EXTRACT function
- D. Mapped dimensions
- E. The VLOOKUP function
正解：A、C、D
解説：
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.
質問 # 49
Which three statements accurately describe the different data stream types in Marketing Cloud intelligence?
- A. All data stream types consist of at least one entity
- B. Every data stream type includes the Medio Buy entity
- C. Each data stream type has its own set of measurements
- D. Each data stream type has Its own main entity
- E. All data stream types share at least one mutual measurement
正解：A、C、D
解説：
In Marketing Cloud Intelligence, data stream types are templates that define how data should be structured within the system. Each data stream type:
* B.Includes at least one entity, which is a fundamental component of the data stream and represents a collection of related data points.
* D.Has its own main entity, which is the primary focus of that particular data stream type and serves as the central point of reference for the associated data.
* E.Contains its own unique set of measurements that are specific to the type of data being captured within that stream. These measurements represent quantitative data that can be analyzed within the context of the main entity and other dimensions present in the data stream.
A is incorrect because not every data stream type includes the Media Buy entity-this is specific to certain types of advertising data streams. C is incorrect because not all data stream types share at least one mutual measurement; measurements are typically unique to the data stream's focus and purpose.
質問 # 50
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.
The client would like to have a "Site Revenue" measurement.
This measurement should return the highest revenue value per Site, for example:
For Site Key 'SK_C_2', the "Site Revenue" should be $7.00.
When aggregated by date, the "Site Revenue" measurement should return the total sum of the results of all sites.
For example:
For the date 1 Apr 2020, "Site Revenue" should be $11.00 (sum of Site Revenue for Site Keys 'SK_C_1' ($4.00) and 'SK_C_2' ($7.00))
Which options will yield the desired result;
- A. Option #2 & Option #4
- B. Option #1 & Option #4
- C. Option #2 & Option #3
- D. Option #1 & Option #3
正解：A
解説：
* Option #2: It suggests using the 'SUM' function to aggregate the 'Site Revenue' for each 'Site Key'. This is necessary to ensure that when aggregated by date, 'Site Revenue' should return the total sum of the highest revenue for all sites.
* Option #4: It indicates changing the Aggregation Function of Revenue to 'MAX' within Data Stream C.
This ensures that for a given 'Site Key', the highest revenue value is selected, which is correct for
* individual site revenue determination.
Combining Option #2 and Option #4 will provide the desired result:
* For an individual 'Site Key', it will give the highest revenue (using MAX aggregation in Option #4).
* When aggregating by date across all 'Site Key's, it will sum the highest revenues (using the SUM function in Option #2).
質問 # 51
......
最新のMarketing-Cloud-Intelligence学習ガイド2024年最新の- 提供するのはテストエンジンとPDF：https://jp.fast2test.com/Marketing-Cloud-Intelligence-premium-file.html