Salesforce Marketing-Cloud-Intelligence Real Exam Questions Guaranteed Updated Dump from DumpsTorrent [Q21-Q39]

Share

Salesforce Marketing-Cloud-Intelligence Real Exam Questions Guaranteed Updated Dump from DumpsTorrent

Verified Pass Marketing-Cloud-Intelligence Exam in First Attempt Guaranteed


Salesforce Marketing-Cloud-Intelligence Exam Syllabus Topics:

TopicDetails
Topic 1
  • QA Ability: This section focuses on common QA steps for various scenarios, enabling Salesforce marketing professionals to ensure data quality and platform performance.
Topic 2
  • 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 3
  • Data Model: In this domain, marketing professionals will explore data model entities, their relationships, and attributes within Marketing Cloud Intelligence.
Topic 4
  • Data Fusion: This topic focuses on the use cases and properties of Data Fusion, equipping marketing professionals to merge datasets effectively for comprehensive marketing insights.
Topic 5
  • 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 6
  • 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 7
  • Data Update Permissions: This area tests knowledge of permissions and settings related to data updates. It includes understanding parent-child setups and managing the "Source of Truth" for data accuracy.
Topic 8
  • General Functionalities: In this topic, Salesforce marketing professionals will explore core functionalities of Marketing Cloud Intelligence. It measures understanding of platform features critical to data-driven marketing strategies and insights.
Topic 9
  • 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.

 

NEW QUESTION # 21
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. Confirmed Interest & Registered
  • C. interest
  • D. Confirmed interest

Answer: A

Explanation:
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.


NEW QUESTION # 22
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. An EXTRACT formula (for Color) was written and mapped to a Creative custom attribute.
  • B. A Harmonized dimension was created via a pattern over the Creative Name.
  • C. A calculated dimension was created with the formula: EXTRACT([Creative_Namel, #1)
  • D. An EXTRACT formula (for Color) was written and mapped to a Media Buy custom attribute.

Answer: A

Explanation:
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.


NEW QUESTION # 23
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 Name, Impressions
  • C. Creative Name, Impressions, Analyzed Impressions
  • D. Media Buy Type, Analyzed Impressions

Answer: A

Explanation:
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.


NEW QUESTION # 24
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. 1
  • C. Depends on the Data Updates Permissions
  • D. 2

Answer: B

Explanation:
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.


NEW QUESTION # 25
A client provides the following two data streams:
Data Stream 1:

Question-
The client would like to use a VLOOKUP formula to calculate the Cost per Campaign Advertiser on January
1st 2020. Which mapping options should the client apply to obtain the expected result?

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
To calculate Cost per Campaign Advertiser using a VLOOKUP formula, the client needs to look up the 'Cost' from Data Stream 2 based on a matching 'Media Buy Name' in Data Stream 1. Option A shows that 'Media Buy Name' is the lookup value, which is correct. The 'Campaign Advertiser' is then linked to the 'Cost' from Data Stream 2 through the VLOOKUP formula applied to the 'Media Buy Custom Attribute 01' in Data Stream 2. This setup will correctly associate the cost with the campaign advertiser.


NEW QUESTION # 26
An implementation engineer has been provided with 4 different source files: 03m 16s
1. Twitter Ads
2. Creative Classification
3. Placement Classification
4, Campaign Category Classification
The main source is Twitter Ads (which includes various fields and KPIs), and the rest are classification files that connect to Twitter Ads and enrich different fields within it.
The connections between the files are described as follows:
1st Party Creative Classification
File structure/headers:

Creative ID - links back to Creative Key (Twitter Ads)
1st Party Placement Classification &
File structure/headers:

Category - links back to Campaign Category (Twitter Ads)
Which proposed solution meets the client's requirements for the above use case?

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
For the given use case, where the Twitter Ads data stream needs to be enriched with classifications from three other sources, the correct implementation would involve creating links between the various fields across these files.
Option A is correct because it shows the correct usage of the fields from the classification files:
"Creative ID" in the Creative Classification file is linked to the "Creative Key" in the Twitter Ads data, allowing for enrichment with creative details.
"Placement ID" in the Placement Classification file is linked to a corresponding field in the Twitter Ads data, allowing for placement details to be added.
"Category" in the Campaign Category Classification file is linked back to "Campaign Category" in the Twitter Ads data, thus enriching the campaign data with the correct categories.
This configuration correctly uses VLOOKUP to enrich the Twitter Ads data stream with additional details from the classification files, aligning with best practices for data integration and enrichment in Marketing Cloud Intelligence.


NEW QUESTION # 27
An implementation engineer is requested to create the harmonization field - Magician This field should come from multiple Twitter Ads data streams, and should follow the below logic:

Using the Harmonization Center, the engineer created a single Pattern for Campaign Name. What other action should the engineer take to meet the requirements?

  • A. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.
  • B. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension
  • C. Create a second Pattern for Media Buy Name
  • D. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for 'Messi' and another for Ronaldo') for the final Harmonized Dimension.

Answer: D

Explanation:
For the field 'Magician', the engineer is required to follow a logic that extracts a value from 'Campaign Name' and checks against a validation list for specific values ('Messi' or 'Ronaldo'). If those values are not found, it should instead extract from 'Media Buy Name'. To accomplish this, the engineer should:
Use the created Pattern for 'Campaign Name'.
Create a second Pattern for 'Media Buy Name' to capture the fallback values.
Apply two Classification Rules to the Harmonized Dimension: one for the value 'Messi' and another for 'Ronaldo'. This is to check the extracted 'Campaign Name' against these specific values.
These steps ensure that the 'Magician' field will be populated with the correct values from the respective data streams following the specified logic.


NEW QUESTION # 28
An implementation engineer is requested to create the harmonization field - Magician This field should come from multiple Twitter Ads data streams, and should follow the below logic:

Using the Harmonization Center, the engineer created a single Pattern for Campaign Name. What other action should the engineer take to meetthe requirements?

  • A. Create a second Pattern for Media Buy Name and add a validation list (with the two values) for the final Harmonized Dimension.
  • B. Create a second Pattern for Media Buy Name and apply a Classification Rule (with the two values) for the final Harmonized Dimension
  • C. Create a second Pattern for Media Buy Name
  • D. Create a second Pattern for Media Buy Name and apply two Classification Rules (one for 'Messi' and another for Ronaldo') for the final Harmonized Dimension.

Answer: D

Explanation:
For the field 'Magician', the engineer is required to follow a logic that extracts a value from 'Campaign Name' and checks against a validation list for specific values ('Messi' or 'Ronaldo'). If those values are not found, it should instead extract from 'Media Buy Name'. To accomplish this, the engineer should:
* Use the created Pattern for 'Campaign Name'.
* Create a second Pattern for 'Media Buy Name' to capture the fallback values.
* Apply two Classification Rules to the Harmonized Dimension: one for the value 'Messi' and another for
'Ronaldo'. This is to check the extracted 'Campaign Name' against these specific values.
These steps ensure that the 'Magician' field will be populated with the correct values from the respective data streams following the specified logic.


NEW QUESTION # 29
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. A mapping formula was populated, indicating not to bring Type! values.
  • B. The measurement 'Clicks' is set as a percentage.
  • C. The aggregation function is set as AVG
  • D. The aggregation function is set as LIFETIME

Answer: C

Explanation:
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. Reference: Salesforce Marketing Cloud Intelligence documentation on custom measurements and data aggregations explains how to set and understand different aggregation functions.


NEW QUESTION # 30
What is the relationship between "Media Buy Key" and "Creative Key?

  • A. One-to-one
  • B. One-to-many (one Media Buy ley has many Creative Key)
  • C. Many-to-one (one Creative Key has many Media Buy Keys)
  • D. Many-to-many

Answer: B

Explanation:
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.


NEW QUESTION # 31
Animplementation engineer has been provided with the below dataset:

*Note: CPC = Cost per Click
Formula: Cost / Clicks
Which action should an engineer take to successfully integrate CPC?

  • A. Unmap it, as Datorama will calculate it automatically.
  • B. Populate the logic within a custom measurement. No need to change Aggregation.
  • C. Populate the logic within a custom measurement. Set Aggregation to SUM.
  • D. Populate the logic within a custom measurement. Set Aggregation to AVG.

Answer: B

Explanation:
CPC (Cost per Click) is a calculated metric that should be created using a custom measurement based on the formula provided (Cost / Clicks). This calculation does not require a change in the aggregation setting because it is derived from other base metrics that are already aggregated appropriately. In Salesforce Marketing Cloud Intelligence, custom measurements are used to create new metrics from existing data points, and the system will use the underlying data's aggregation to perform the calculation. References: Salesforce Marketing Cloud Intelligence documentation on creating custom measurements and calculated metrics.


NEW QUESTION # 32
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. Main entity is not mapped
  • B. All mapped Measurements for a given row have values equal to zero
  • C. The file does not contain any measurements (dimension only)
  • D. The source file does not contain the mediaBuy entity

Answer: A,B

Explanation:
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.


NEW QUESTION # 33
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 3
  • B. Option 5
  • C. Option 4
  • D. Option 1
  • E. Option 2

Answer: B,D

Explanation:
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.


NEW QUESTION # 34

Which option will yield the desiredresult:?

  • A. Option 3
  • B. Option 4
  • C. Option 1
  • D. Option 2

Answer: B

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 # 35
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

Answer: B

Explanation:
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'.


NEW QUESTION # 36
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. 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'.
  • B. 'Meeting Code' will be mapped to 'Main Generic Entity Attribute 1'.
    'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
  • 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 Key'.
    'Meeting Name' will be mapped to 'Generic Entity 2 Key'.
  • E. 'Meeting Code' will be mapped to 'Main Generic Entity Key'.
    'Meeting Name' will be mapped to 'Main Generic Entity custom attribute'.

Answer: A,E

Explanation:
'Meeting Name' will be mapped to 'Main Generic Entity Attribute 2'.
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 # 37
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

Answer: D

Explanation:
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.


NEW QUESTION # 38
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 Name.
  • C. All fields are mapped except for the Media Buy Key.
  • D. All fields are mapped except for the Creative Name

Answer: A

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 # 39
......

Download Real Salesforce Marketing-Cloud-Intelligence Exam Dumps Test Engine Exam Questions: https://guidetorrent.dumpstorrent.com/Marketing-Cloud-Intelligence-exam-prep.html