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Marketing-Cloud-Intelligence認定有効な試験問題集解答で学習ガイド！（最新の64問題)
質問 # 16
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. Depends on the Data Updates Permissions
- C. 1
- D. 2
正解：D
解説：
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.
質問 # 17
What is the relationship between "Media Buy Key" and "Campaign Key"?
- A. Many-to-many
- B. One-to-many (one Media Buy Key has many Campaign Keys)
- C. Many-to-one (one Campaign Key has many Media Buy Keys)
- D. One-to-one
正解：C
解説：
Typically, 'Campaign Key' is a unique identifier for a specific marketing campaign, while 'Media Buy Key' refers to the purchases of advertising space associated with that campaign. A campaign can have multiple media buys, so the relationship is many-to-one, with many media buys (Media Buy Keys) associated with a single campaign (Campaign Key).
質問 # 18
Which three statements accurately describe the different data stream types in Marketing Cloud intelligence?
- A. Every data stream type includes the Medio Buy entity
- B. Each data stream type has Its own main entity
- C. All data stream types share at least one mutual measurement
- D. Each data stream type has its own set of measurements
- E. All data stream types consist of at least one entity
正解：B、D、E
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 # 19
Which two statements are correct regarding LiteConnect?
- A. It does not require any identification of entities, keys or any other categorization.
- B. Data coming from LiteConnect cannot be harmonized with the rest of the workspace data via the harmonization center at a later step.
- C. The dataset does not conform to the standard data model
- D. All of the dimensions mapped within a LiteConnect data stream are considered overarching entities.
正解：A、C
解説：
LiteConnect is a feature in Salesforce Marketing Cloud Intelligence that allows users to bring external data into the platform quickly and easily. Here are the correct statements regarding LiteConnect:
* A.LiteConnect allows for a quick setup by not requiring detailed identification of entities, keys, or categorization. Users can upload files without having to conform to the standard data model, which speeds up the process of data integration.
* B.With LiteConnect, datasets are uploaded in their native format and do not conform to the standard data model of Marketing Cloud Intelligence. This means that the original structure of the dataset is maintained, and there is no need for extensive transformation or mapping upon the initial data import.
For C and D: While LiteConnect datasets might not conform to the standard data model initially, there are capabilities within Marketing Cloud Intelligence to further categorize and harmonize this data if needed.
Therefore, C is not entirely correct, and D is incorrect because harmonization can indeed occur at a later step.
質問 # 20
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. Data Classification
- C. Custom 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 # 21
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:
The client performed the below standard mapping:
As a final step, the client had created the field 'Color'. As can be seen, it is extracted from the Creative Name (after the '#' sign).
For QA purposes, you have queried a pivot table, with the following fields:
* Media Buy Key
* Media Buy Name
* In View Impressions
The final pivot is presented below:
- A. A Harmonized dimension was created via a pattern over the Creative Name.
- B. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
- C. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
- D. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
正解：D
解説：
Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.
質問 # 22
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 assume that the file is mapped in the OPPORTUNITIES Data Stream type with the following mapping:
"Day" - "Created Date"
"Opportunity Key" + Opportunity Key
"Opportunity Stage" - Opportunity Stage
A pivot table was created to present the count of opportunities in each stage. The pivot table is filtered on Jan
11th. What is the number of'opportunitiesin the Confirmed Interest stage?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
pivot table is filtered on January 11th, we refer to the Opportunity file and see that there are no records for January 11th. Thus, there would be zero opportunities in the Confirmed Interest stage on that date. The Salesforce Marketing Cloud Intelligence's pivot table feature allows for the display of counts of entities based on the filtered criteria, which in this scenario would show zero since no records exist for the filtered date.
Reference: Salesforce Marketing Cloud Intelligence documentation on pivot table functionalities.
質問 # 23
Aclient has integrated the following files:
File A:
File B:
The client would like to link the two files in order to view the two KPIs ('Tasks Completed' and 'Tasks Assigned) alongside 'Employee Name' and/or
'Squad'.
The client set the following properties:
+ File Ais set as the Parent data stream
* Both files were uploaded to a generic data stream type.
* Override Media Buy Hierarchies is checked for file A.
* The 'Data Updates Permissions' set for file B is 'Update Attributes and Hierarchy'.
When filtering on the entire date range (1-30/8), and querying employee ID, Name and Squad with the two measurements - what will the result look like?
- A.
- B.
- C.
- D.
正解：C
解説：
In Marketing Cloud Intelligence, when linking two data streams, the parent data stream (File A) provides the main structure. Since 'Override Media Buy Hierarchies' is checked for File A, the hierarchies from File B will be aligned with File A. Given 'Data UpdatesPermissions' set for file B as 'Update Attributes and Hierarchy', this means that attributes and hierarchy will be updated in the parent file based on the child file (File B), but the child file's metrics won't be associated with the parent file's date.
Hence, when filtering on the entire date range (1-30/8), the resulting view will align with the structure of the parent data stream, showing the KPIs ('Tasks Completed' from File A and 'Tasks Assigned' from File B) alongside the employee names and squads from the respective files. Since the employee IDs align, the data can be linked properly. However, since the dates do not align (File A data is from 01/08/2019 and File B from
15/08/2019), only attributes from File B will be updated without date association.
The result will look like Option C, where the employee names are corrected based on File B's data, the squads are added from File B, and the tasks_completed and tasks_assigned are displayed from their respective files.
The tasks_assigned from File B are shown without date association as File B's date doesn't match with File A's.
質問 # 24
What are two potential reasons for performance issues (when loading a dashboard) when using the CRM data stream type?
- A. Pacing - daily rows are being created for every lead and opportunity keys
- B. No mappable measurements - all measurements are calculated
- C. When a data stream type ''CRM - Leads' is created, another complementary 'CRM - Opportunity' is created automatically.
- D. The data is stored at the workspace level.
正解：A、B
質問 # 25
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 hasa requirement to be able to view measurements from all data sources sliced by Exam Topic values as seen in the following example:
Which harmonization feature should an Implementation engineer use to meet the client's requirement?
- A. Parent Chile
- B. Custom Classification
- C. Transformers
- D. Calculated dimensions
- E. Fusion
正解：B
解説：
To meet the client's requirement of slicing measurements by 'Exam Topic' values, an Implementation Engineer should use Custom Classification. This feature allows different Exam IDs to be classified into their respective Exam Topics, ensuring that data from all sources can be accurately harmonized and analyzed based on these topics.
質問 # 26
A client Ingested the following We into Marketing Cloud Intelligence:
The mapping of the above file can be seen below:
Date - Day
Media Buy Key - Media Buy Key
Campaign Name - Campaign Name
Campaign Group -. Campaign Custom Attribute 01
Clicks -> Clicks
Media Cost -> Media Cost
Campaign Planned Clicks -> Delivery Custom Metric 01
The client would like to have a "Campaign Planned Clicks" measurement.
This measurement should return the "Campaign Planned Clicks" value per Campaign, for example:
For Campaign Name 'Campaign AAA", the "Campaign Planned Clicks" should be 2000, rather than 6000 (the total sum by the number of Media Buy keys).
In order to create this measurement, the client considered multiple approaches. Please review the different approaches and answer the following question:
Which two options will yield a false result:
- A. Option 4
- B. Option 2
- C. Option 1
- D. Option 5
- E. Option 3
正解：C、D
解説：
The goal is to obtain a "Campaign Planned Clicks" value per Campaign, not accumulated by Media Buy keys.
Option 1 (SUM aggregation function) would sum all the "Campaign Planned Clicks" across Media Buy keys which would not yield the unique value per Campaign. Similarly, Option 5 (AVG aggregation function at Campaign Key level) would incorrectly average the values. Both options do not provide a way to return a singular "Campaign Planned Clicks" value for each Campaign.
質問 # 27
An implementation engineer is requested to integrate the following files:
File A:
File B:
The client would like to link the two files in order to view the two KPIS (Tasks Completed' and 'tasks Assignmed') alongside'Employee Name' and/or 'Squard'.
A Parent-Child configuration was set between the two.
Which two statements are correct?
- A. The two files were uploaded to a different Generic type
- B. The two files cannot be Joined as they hold different measurements
- C. The join can be successful even if "empjd' isn't mapped and employee.name' is mapped to the same entity name in both data streams
- D. Any one of the files can potentially be set as the Parent data stream
- E. The two files cannot be joined as they hold different dates
正解：C、D
解説：
In Marketing Cloud Intelligence, joining two files requires a common field to be mapped as the same entity. If
"employee_name" is consistently mapped across both data streams, it can serve as the basis for the join, regardless of whether "employee_id" is mapped. The choice of which file serves as the Parent stream depends on the use case and the desired reporting structure, but technically, either could serve as the Parent.
質問 # 28
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" - Main Generic Entity Attribute
"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 Jan
11th. What is the number of 'opportunities in the Confirmed Interest stage?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
Based on the data provided and the date filter set to January 11th, there are no records for 'Confirmed Interest' on that specific date in the Opportunity file. Thus, the number of opportunities in the 'Confirmed Interest' stage for January 11th would be zero (0). In Salesforce Marketing Cloud Intelligence, when creating pivot tables, the data is aggregated based on the selected filters. If no records meet the filter criteria, the result for that category would be zero. The answer is supported by best practices in data analysis and reporting within Salesforce Marketing Cloud Intelligence, where date filters are applied to segment and analyze data.
質問 # 29
An implementation engineer has been asked to perform a QA for a newly created harmonization field, Color, implemented by a client.
The source file that was ingested can be seen below:
The client performed the below standard mapping:
As a final step, the client had created the field 'Color'. As can be seen, it is extracted from the Creative Name (after the '#' sign).
For QA purposes, you have queried a pivot table, with the following fields:
* Media Buy Key
* Media Buy Name
* In View Impressions
The final pivot is presented below:
- A. A Harmonized dimension was created via a pattern over the Creative Name.
- B. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
- C. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.
- D. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
正解：D
解説：
Given that the 'Color' field is extracted from the 'Creative Name' field and appears to be part of the creative-level data, the most logical method would be to create an EXTRACT formula and map it to a Creative custom attribute. This allows the 'Color' value to be associated directly with each creative entry. In Salesforce Marketing Cloud Intelligence, the EXTRACT formula can be used to parse and segment text strings within a field, and this process is used for harmonizing data by creating new dimensions or attributes based on existing data, which is what's described here. This answer is consistent with Salesforce Marketing Cloud Intelligence features that enable data transformation and harmonization through formulaic mapping, as per the official Salesforce documentation on data harmonization and transformation.
質問 # 30
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" - Main Generic Entity Attribute
"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 Jan 11th. What is the number of 'opportunities in the Confirmed Interest stage?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
Based on the data provided and the date filter set to January 11th, there are no records for 'Confirmed Interest' on that specific date in the Opportunity file. Thus, the number of opportunities in the 'Confirmed Interest' stage for January 11th would be zero (0). In Salesforce Marketing Cloud Intelligence, when creating pivot tables, the data is aggregated based on the selected filters. If no records meet the filter criteria, the result for that category would be zero. The answer is supported by best practices in data analysis and reporting within Salesforce Marketing Cloud Intelligence, where date filters are applied to segment and analyze data.
質問 # 31
Which two statements are correct regarding the Parent-Child configuration?
- A. A Parent-Child cannot be configured between an Ads data stream type and a Conversion Tag one.
- B. Parent-Child allows sharing both dimensions and measurements
- C. Parent-Child configurations can cause performances issues
- D. Parent-Child links different tables based on shared key values
正解：C、D
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 # 32
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. 0
- B. 1
- C. 2
- D. N-A
正解：B
解説：
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.
質問 # 33
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. Ease of Maintenance - thelogic is written and populated in one centralized place
- B. Performance (Performance when loading a dashboard page) should be optimized as the values of calculated dimensions are stored within the database.
- C. Processing - creation of Calculated Dimensions will ease the processing time of the data streams it relates to
- D. Data model restrictions - Calculated Dimensions do not need to adhere to Marketing Cloud Intelligence's data model
- E. Scalability - future data streams that will follow similar logic will be automatically harmonized.
正解：A、B、E
解説：
* 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.
質問 # 34
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. interest
- C. Confirmed interest
- D. Confirmed Interest & Registered
正解：A
解説：
Filtering the pivot table on January 7th-11th, we see that the Opportunity Key 123AA01 appears on January
6th with the stage 'Interest' and then on January 10th with the stage 'Registered'. Even though the 'Interest' stage is not within the filtered dates, it is the initial stage of the opportunity, so it should be counted along with the 'Registered' stage which falls within the filter range.
質問 # 35
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)
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 Linkedin or AdRoll sources).
Another single pattern for Campaign Name (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
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 # 36
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 Campaign Key
- B. All fields are mapped except for the Media Buy Key.
- C. All fields are mapped except for the Media Buy Name.
- D. All fields are mapped except for the Creative Name
正解：A
解説：
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.
質問 # 37
A client's data consists of three data sources - Facebook Ads, LinkedIn Ads and Google Campaign Manager.
Notes:
* The client is planning on adding an additional 100 Facebook Ads data streams and 50 more LinkedIn Ads data streams.
* The final volume of data in the workspace will be 5M rows
* Each data source has a naming convention and it can be assumed that any additional profile (i.e. Data Stream) from one of these sources will follow the same naming convention.
The client provided the following sample files:
Facebook Ads:
The client would like to create a new harmonization field named "Market," which will only be coming from Facebook Ads and LinkedIn Ads. The logic for
"Market" is the following:
IF Media Buy Type is equal to "TypeB" or "TypeC" or "TypeD"
Return 'Europe'
ELSE
Return 'Rest Of The World'
In order to create the harmonization field Market, the client considers using either Mapping Formula, Calculated Dimension, VLOOKUP or Patterns.
Considering maintenance and scalability, which option is recommended?
- A. vLookuP
- B. Patterns
- C. Calculated Dimension
- D. Mapping Formulas
正解：B
解説：
Patterns are the best approach in this scenario because:
Scalability: Patterns are highly scalable and can easily handle the addition of 100 more Facebook Ads and 50 more LinkedIn Ads streams. You can define pattern-matching rules that automatically apply to new data streams based on the naming conventions.
Flexibility and Maintenance: Patterns allow you to maintain and adjust logic easily. Since the logic for determining "Market" is based on a defined naming convention (e.g., Media Buy Type), Patterns can handle these rules effectively without requiring manual updates or static tables.
Efficient Harmonization: Patterns automatically classify data based on defined rules, reducing the need for ongoing manual maintenance compared to approaches like VLOOKUP or Mapping Formulas, which might require frequent updates as data changes.
Why not other options?
Mapping Formulas: While Mapping Formulas work well for static mappings, they are not as scalable or maintainable when the dataset grows or changes frequently.
Calculated Dimension: This option is valid for simple logic but is less maintainable for large-scale datasets, especially when new data streams are added.
VLOOKUP: This method is manual and not scalable. It would require you to update lookup tables for each new data stream, which is inefficient given the expected growth of the data.
質問 # 38
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 #2 & Option #3
- C. Option #1 & Option #3
- D. Option #1 & Option #4
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 # 39
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" - Main Generic Entity Attribute
"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 Jan
11th. What is the number of opportunities in the Interest stage?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
Since the pivot table is filtered on January 11th and the provided Opportunity file does not show any records dated January 11th, there are zero opportunities in the Interest stage for that date. Salesforce Marketing Cloud Intelligence allows users to create pivot tables and filter data basedon specific criteria, such as dates. In this case, the filter would exclude all rows that do not match the specified date, resulting in a count of zero for the Interest stage. This would apply to any stage since there are no records for January 11th. Reference can be made to Salesforce Marketing Cloud Intelligence documentation on filtering and pivot tables.
質問 # 40
A client created a new KPI: CPS (Cost per Sign-up).
The new KIP is mapped within the data stream mapping, and is populated with the following logic: (Media Cost) / Sign-ups) As can be seen in the table below, CPS was created twice and was set with two different aggregations:
From looking at the table, what are the aggregation settings for each one of the newly created KPIs?
- A.
- B.
- C.
- D.
正解：D
解説：
The KPI CPS (Cost per Sign-up) would be calculated by dividing the 'Media Cost' by 'Sign-ups'. The table indicates that CPS is set with two different aggregations. In option C, CPS #1 is set to 'AUTO', which allows the system to decide the best aggregation method based on the context. CPS #2 is set to 'SUM', which indicates that the individual costs per sign-up are summed up across multiple records to provide a total cost per sign-up.
質問 # 41
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Salesforce Marketing-Cloud-Intelligence 認定試験の出題範囲：
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