Craig Boyd Craig Boyd's Profile Page

Craig Boyd Craig Boyd

0 Course Enrolled • 0 Course Completed

Biography

Data-Cloud-Consultant Latest Dumps Questions, Data-Cloud-Consultant New Study Notes

Our company was built in 2008 since all our education experts have more than ten years' experience in Data-Cloud-Consultant guide torrent. The most important characters we pay attention on are our quality and pass rate. We devote ourselves to improve passing rate constantly and service satisfaction degree of our Data-Cloud-Consultant training guide. And now you can find the data provided from our loyal customers that our pass rate of Data-Cloud-Consultant learning guide is more than 98%. You will successfully pass your Data-Cloud-Consultant exam for sure.

Salesforce Data-Cloud-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

Data Cloud Overview: This topic covers Data Cloud's function, key terminology, business value, typical use cases, the Data Cloud lifecycle, dependencies, and principles of data ethics. These sub-topics provide an overview of Data Cloud's capabilities and applications.

Topic 2

Segmentation and Insights: This topic defines basic concepts of segmentation and use cases, identifies scenarios for analyzing segment membership, configuring, refining, and maintaining segments within Data Cloud, and differentiating between calculated and streaming insights.

Topic 3

Data Cloud Setup and Administration: This topic includes applying Data Cloud permissions, permission sets, org-wide settings. It describes and configures data stream types, and data bundles. Moreover, it discusses use cases for data spaces, creating data spaces, managing and administering Data Cloud using reports, dashboards, flows, packaging, data kits, diagnosing and exploring data using Data Explorer, Profile Explorer, and APIs.

Topic 4

Identity Resolution: It describes matching and how its rule sets are applied. Furthermore, it discusses reconciling data and its rule sets, the results of identity resolution, and use cases.

Topic 5

Data Ingestion and Modeling: This topic covers the different transformation capabilities within Data Cloud. It includes describing processes and considerations for data ingestion from various sources, defining, mapping, and modeling data using best practices aligned with identity resolution. Lastly, it discusses using available tools to inspect and validate ingested and modeled data.

>> Data-Cloud-Consultant Latest Dumps Questions <<

Data-Cloud-Consultant New Study Notes | Test Data-Cloud-Consultant Preparation

Our Data-Cloud-Consultant study materials include 3 versions and they are the PDF version, PC version, APP online version. You can understand each version’s merits and using method in detail before you decide to buy our Data-Cloud-Consultant learning guide. And the content of the three different versions is the same, but the displays are totally different according to the study interest and hobbies. And it is quite enjoyable to learn with our Data-Cloud-Consultant Exam Questions.

Salesforce Certified Data Cloud Consultant Sample Questions (Q65-Q70):

NEW QUESTION # 65
A customer has a calculated insight about lifetime value.
What does the consultant need to be aware of if the calculated insight.
needs to be modified?

A. New dimensions can be added.
B. Existing measures can be removed.
C. Existing dimensions can be removed.
D. New measures can be added.

Answer: C

Explanation:
A calculated insight is a multidimensional metric that is defined and calculated from data using SQL expressions. A calculated insight can include dimensions and measures. Dimensions are the fields that are used to group or filter the data, such as customer ID, product category, or region. Measures are the fields that are used to perform calculations or aggregations, such as revenue, quantity, or average order value. A calculated insight can be modified by editing the SQL expression or changing the data space. However, the consultant needs to be aware of the following limitations and considerations when modifying a calculated insight12:
Existing dimensions cannot be removed. If a dimension is removed from the SQL expression, the calculated insight will fail to run and display an error message. This is because the dimension is used to create the primary key for the calculated insight object, and removing it will cause a conflict with the existing data. Therefore, the correct answer is B.
New dimensions can be added. If a dimension is added to the SQL expression, the calculated insight will run and create a new field for the dimension in the calculated insight object. However, the consultant should be careful not to add too many dimensions, as this can affect the performance and usability of the calculated insight.
Existing measures can be removed. If a measure is removed from the SQL expression, the calculated insight will run and delete the field for the measure from the calculated insight object. However, the consultant should be aware that removing a measure can affect the existing segments or activations that use the calculated insight.
New measures can be added. If a measure is added to the SQL expression, the calculated insight will run and create a new field for the measure in the calculated insight object. However, the consultant should be careful not to add too many measures, as this can affect the performance and usability of the calculated insight. Reference: Calculated Insights, Calculated Insights in a Data Space.

NEW QUESTION # 66
A consultant wants to ensure that every segment managed by multiple brand teams adheres to the same set of exclusion criteria, that are updated on a monthly basis.
What is the most efficient option to allow for this capability?

A. Create a segment and copy it for each brand.
B. Create, publish, and deploy a data kit.
C. Create a nested segment.
D. Create a reusable container block with common criteria.

Answer: D

Explanation:
The most efficient option to allow for this capability is to create a reusable container block with common criteria. A container block is a segment component that can be reused across multiple segments. A container block can contain any combination of filters, nested segments, and exclusion criteria. A consultant can create a container block with the exclusion criteria that apply to all the segments managed by multiple brand teams, and then add the container block to each segment. This way, the consultant can update the exclusion criteria in one place and have them reflected in all the segments that use the container block.
The other options are not the most efficient options to allow for this capability. Creating, publishing, and deploying a data kit is a way to share data and segments across different data spaces, but it does not allow for updating the exclusion criteria on a monthly basis. Creating a nested segment is a way to combine segments using logical operators, but it does not allow for excluding individuals based on specific criteria. Creating a segment and copying it for each brand is a way to create multiple segments with the same exclusion criteria, but it does not allow for updating the exclusion criteria in one place.
References:
* Create a Container Block
* Create a Segment in Data Cloud
* Create and Publish a Data Kit
* Create a Nested Segment

NEW QUESTION # 67
Cumulus Financial segregates its sales CRM data based on Region for its Data Cloud users. Multiple data spaces are configured: a default space and two additional spaces tailored for EMEA and APAC regions.
EME A sales reps who need temporary access to visualize data for both regions say that they cannot visualize APAC dat a. APAC sales reps can visualize the corresponding segmented data.
Which statement describes the cause of this issue?

A. The APAC data space Is not associated with any profile.
B. The EMEA sales reps have not been assigned to the profile associated with the APAC data space.
C. The EMEA sales reps have not been assigned to the permission set associated with the APAC data space.
D. The APAC data space is not associated with any permission set.

Answer: C

NEW QUESTION # 68
Luxury Retailers created a segment targeting high value customers that it activates through Marketing Cloud for email communication. The company notices that the activated count is smaller than the segment count.
What is a reason for this?

A. Marketing Cloud activations apply a frequency cap and limit the number of records that can be sent in an activation.
B. Marketing Cloud activations only activate those individuals that already exist in Marketing Cloud. They do not allow activation of new records.
C. Marketing Cloud activations automatically suppress individuals who are unengaged and have not opened or clicked on an email in the last six months.
D. Data Cloud enforces the presence of Contact Point for Marketing Cloud activations. If the individual does not have a related Contact Point, it will not be activated.

Answer: D

Explanation:
The reason for the activated count being smaller than the segment count is A. Data Cloud enforces the presence of Contact Point for Marketing Cloud activations. If the individual does not have a related Contact Point, it will not be activated. A Contact Point is a data model object that represents a channel or method of communication with an individual, such as email, phone, or social media. For Marketing Cloud activations, Data Cloud requires that the individual has a related Contact Point of type Email, which contains a valid email address. If the individual does not have such a Contact Point, or if the Contact Point is missing or invalid, the individual will not be activated and will not receive the email communication. Therefore, the activated count may be lower than the segment count, depending on how many individuals in the segment have a valid email Contact Point. References: Salesforce Data Cloud Consultant Exam Guide, Contact Point, Marketing Cloud Activation

NEW QUESTION # 69
A company wants to include certain personalized fields in an email by including related attributes during the activation in Data Cloud. It notices that some values, such as purchased product names, do not have consistent casing in Marketing Cloud Engagement. For example, purchased product names appear as follows: Jacket, jacket, shoes, SHOES. The company wants to normalize all names to proper case and replace any null values with a default value.
How should a consultant fulfill this requirement within Data Cloud?

A. Use formula fields when ingesting at the data stream level.
B. Create one batch data transform per data stream.
C. Create a streaming insight with a data action.
D. Create one batch data transform that creates a new DLO.

Answer: D

Explanation:
To normalize purchased product names (e.g., converting casing to proper case and replacing null values with a default value) within Salesforce Data Cloud, the best approach is to create a batch data transform that generates a new DLO. Here's the detailed explanation:
Understanding the Problem :
The company wants to ensure that product names in Marketing Cloud Engagement are consistent and properly formatted. The inconsistencies in casing (e.g., "Jacket," "jacket," "shoes," "SHOES") and the presence of null values need to be addressed before activation.
Why Batch Data Transform?
A batch data transform allows you to process large volumes of data in bulk, making it ideal for cleaning and normalizing datasets.
By creating a new DLO, you ensure that the original data remains intact while providing a clean, transformed dataset for downstream use cases like email personalization.
Steps to Implement This Solution :
Step 1: Navigate to the Data Streams section in Salesforce Data Cloud and identify the data stream containing the purchased product names.
Step 2: Create a new batch data transform by selecting the relevant data stream as the source.
Step 3: Use transformation functions to normalize the product names:
Apply the PROPER() function to convert all product names to proper case.
Use the COALESCE() function to replace null values with a default value (e.g., "Unknown Product").
Step 4: Configure the batch data transform to output the results into a new DLO . This ensures that the transformed data is stored separately from the original dataset.
Step 5: Activate the new DLO for use in Marketing Cloud Engagement. Ensure that the email templates pull product names from the transformed DLO instead of the original dataset.
Why Not Other Options?
A . Create a streaming insight with a data action: Streaming insights are designed for real-time processing and are not suitable for bulk transformations like normalizing casing or replacing null values.
B . Use formula fields when ingesting at the data stream level: Formula fields are useful for simple calculations but are limited in scope and cannot handle complex transformations like null value replacement. Additionally, modifying the ingestion process may not be feasible if the data stream is already in use.
C . Create one batch data transform per data stream: This approach is inefficient and redundant. Instead of creating multiple transforms, a single batch transform can handle all the required changes and output a unified, clean dataset.
By creating a batch data transform that generates a new DLO, the company ensures that the product names are consistently formatted and ready for use in personalized emails, improving the overall customer experience.

NEW QUESTION # 70
......

You do not need to enroll yourself in expensive Data-Cloud-Consultant exam training classes. With the Salesforce Data-Cloud-Consultant valid dumps, you can easily prepare well for the actual Data-Cloud-Consultant exam at home. Do you feel Data-Cloud-Consultant Exam Preparation is tough? Exam-Killer desktop and web-based online Salesforce Data-Cloud-Consultant practice test software will give you a clear idea about the final Data-Cloud-Consultant test pattern.

Data-Cloud-Consultant New Study Notes: https://www.exam-killer.com/Data-Cloud-Consultant-valid-questions.html