[2024年06月25日]Marketing-Cloud-Intelligence試験問題集PDF正確率保証と更新された問題
合格させるMarketing-Cloud-Intelligence試験にはリアルテストエンジンPDFには63問題あります
質問 # 21
An implementation engineer is requested to apply the following logic:
To apply the above logic, the engineer used only the Harmonization Center, without any mapping manipulations. What is the minimum amount of Patterns creating both 'Platform' and 'Line of Business'?"
- A. 0
- B. 1
- C. 2
- D. 3
正解：D
解説：
To create both 'Platform' and 'Line of Business' fields using Patterns in the Harmonization Center without mapping manipulations, the engineer would need to create separate patterns for each data source mentioned.
According to the provided images:
* One pattern for LinkedIn Ads, to extract the 'Campaign Name' at position 4 for the Platform and 'Media Buy Name' at position 7 for Line of Business.
* One pattern for AdRoll, to extract 'Media Buy Name' at position 3 for Platform and at position 2 for Line of Business.
* One pattern for Google Analytics, which seems not required for the Platform but could apply if the Line of Business extraction is necessary, although it states N/A.
Hence, a minimum of 3 patterns would be necessary to create the fields required.
質問 # 22 
Which option will yield the desiredresult:?
- A. Option 2
- B. Option 1
- C. Option 3
- D. Option 4
正解：D
解説：
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.
質問 # 23
What is the relationship between "Media Buy Key" and "Creative Key?
- A. One-to-one
- B. Many-to-one (one Creative Key has many Media Buy Keys)
- C. One-to-many (one Media Buy ley has many Creative Key)
- D. Many-to-many
正解：C
解説：
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.
質問 # 24
Which two statements are correct regarding variable Dimensions in marketing Cloud intelligence's data model?
- A. Variable Dimensions hold a Many-to-Many relationship with its main entity
- B. These dimensions are stored at the workspace level
- C. All variables exist in every data set type, hence are considered as overarching dimensions
- D. These are stand alone dimensions that pertain to the data set itself rather than to a specific entity
正解：A、B
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 # 25
What areunstable measurements?
- A. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'None'.
- B. Measurements for which Aggregation Settings are set as 'Auto' and Granularity is set as 'None'.
- C. Measurements for which Aggregation Settings are set as 'Not Auto' and Granularity is set as 'Not Empty'.
- D. Measurements that are set with the LIFETIME aggregation function
正解：A
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 # 26
Aclient's data consists of three data streams as follows:
* 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.
Which data stream should be set as a parent?
- A. Any of the data streams can technically be the parent
- B. Data Stream C
- C. Data Stream B
- D. Data Stream A
正解：B
解説：
Since Data Stream C is considered the source of truth for both dimensions and measurements, it should be set as the parent data stream. This is because the parent data stream is used as the primary source for hierarchical and attribute data within a parent-child relationship setup. As the source of truth, Data Stream C will provide the foundational data upon which the other streams can be aligned and will ensure consistency and accuracy across the linked data.
質問 # 27
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. Fusion
- B. Parent Chile
- C. Calculated dimensions
- D. Transformers
- E. Custom Classification
正解：E
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 # 28
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 #3
- C. Option #1 & Option #4
- D. Option #2 & 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 # 29
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 Jan
7th - 10th. How many different stages are presented in the table?
- A. 0
- B. 1
- C. 2
- D. 3
正解：C
解説：
Based on the Opportunity file and considering the filter dates from January 7th to 10th, the different stages presented are 'Interest', 'Confirmed Interest', and 'Registered'. This makes a total of 3 different stages that would be presented in the pivot table. Salesforce Marketing CloudIntelligence allows for the creation of pivot tables that can display counts of entities across different dimensions, in this case, Opportunity Stages.
Reference to Salesforce Marketing Cloud Intelligence documentation that covers data mapping and pivot table creation would support this conclusion.
質問 # 30
A client would like to integrate the following two sources:
Google Campaign Manager:
IAS:
After configuring a Parent-Child relationship between the files, which query should an implementation engineer run in order to QA the setup?
- A. Media Buy Type, Media Buy Name, Impressions, Analyzed Impressions
- B. Media Buy Type, Analyzed Impressions
- C. Media Buy Name, Impressions
- D. Creative Name, Impressions, Analyzed Impressions
正解：A
解説：
To QA the Parent-Child relationship setup between Google Campaign Manager and IAS data sources, it is essential to query fields that are common to both sources and that are relevant to the relationship. 'Media Buy Type' and 'Media Buy Name' are common identifiers between the two datasets. 'Impressions' from the Google Campaign Manager and 'Analyzed Impressions' from the IAS data are the metrics that should be compared to ensure they match or correlate as expected due to the Parent-Child relationship. The QA process involves checking that the data is correctly aligned and that the metrics from the parent source (Google Campaign Manager) are properly related to the metrics from the child source (IAS). References: Salesforce Marketing Cloud Intelligence documentation on data integration, Parent-Child relationships, and QA procedures for data setup.
質問 # 31
An implementation engineer is requested to extract the first three-letter segment of the Campaign Name values.
For example:
Campaign Name: AFD@Mulop-1290
Desired outcome: AFD
Other examples:
Which formula will return the desired values?
- A. EXTRACT(csv[campaign_name'],-,0)
- B. EXTRACT(EXTRACT(csv['campaign_name]]/@',1),-,0)
- C. LEFT(EXTRACT(csv[campaign_name'}/-',1),3)
- D. EXTRACT(csv[campaign_name!;@',1)
- E. LEFT(EXTRACT(csy['campaign_name]],~',0),3)
正解：D
解説：
The EXTRACT function is used to split a string based on a delimiter and return the segment at the specified position. The campaign names are structured with the segment of interest followed by an '@' sign. Therefore, the formula needs to extract the segment before the '@'.
* The correct formula is: EXTRACT(csv['campaign_name']; '@', 1). This will take the 'campaign_name' field, split it at the '@' sign, and return the first segment (position 1), which is the three-letter code that is required. The other options are incorrect because they do not properly specify the delimiter and the segment position in the way needed to achieve the desired outcome.
質問 # 32
The following file was uploaded into Marketing Cloud Intelligence as a Generic Data Stream type:
The mapping is as follows:
Day - Day
web_site_key -> Main Generic Entity Key
web_site_name -> Main Generic Entity Name
Web_site_source -> Main Generic Entity Attribute 01
Page Views - Generic Metric 1
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：
With the uploaded file mapped as a Generic Data Stream type, the unique identifier for a row is the combination of 'Day', 'web_site_key', 'web_site_name', and'Web_site_source'. As 'Day' is mapped to 'Day',
'web_site_key' to 'Main Generic Entity Key', 'web_site_name' to 'Main Generic Entity Name', and
'Web_site_source' to 'Main Generic Entity Attribute 01', each unique combination of these fields will constitute a separate row.
The provided file has 4 unique combinations of 'Day', 'web_site_key', 'web_site_name', and 'Web_site_source', as each line has a unique 'web_site_key' and 'web_site_name'. Consequently, Marketing Cloud Intelligence will store 4 rows, one for each unique combination.
質問 # 33
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 # 34
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 links different tables based on shared key values
- C. Parent-Child allows sharing both dimensions and measurements
- D. Parent-Child configurations can cause performances issues
正解：B、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 # 35
Animplementation engineer is requested to extract the second position
of the Campaign Name values.
The Campaign values consist of multiple delimiter types, as can be
seen in the following example:
Campaign Name: Ad15X2w&Delux_wal90
Desired value: Delux
Which three harmonization methods will achieve the desired outcome?
- A. Patterns
- B. Vlookup 0
- C. Calculated Dimensions
- D. Data Fusion
- E. Mapping formula
正解：A、C、E
解説：
To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:
* Calculated Dimensions:These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract
* segments of a string based on delimiters.
* Patterns:This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.
* Mapping Formula:Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly withindata streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.
These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.
質問 # 36
Which two statements are correct regarding LiteConnect?
- A. All of the dimensions mapped within a LiteConnect data stream are considered overarching entities.
- B. It does not require any identification of entities, keys or any other categorization.
- C. Data coming from LiteConnect cannot be harmonized with the rest of the workspace data via the harmonization center at a later step.
- D. The dataset does not conform to the standard data model
正解：B、D
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 # 37
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. All data stream types consist of at least one entity
- C. All data stream types share at least one mutual measurement
- D. Each data stream type has its own set of measurements
- E. Each data stream type has Its own main 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 # 38
Which Marketing Cloud Intelligence field is considered an attribute and not a "variable"?
- A. Campaign Category
- B. Device Browser
- C. Device Category
- D. Geo Location
正解：C
解説：
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.
質問 # 39
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