Salesforce Certified Data 360 Consultant Data-Con-101 Exam Questions

Answer : A

Ensuring High Availability and Fault Tolerance:

High availability refers to systems that are continuously operational and accessible, while fault tolerance is the ability to continue functioning in the event of a failure.

Data Distribution Across Multiple Regions and Data Centers:

Salesforce Data Cloud ensures high availability by replicating data across multiple geographic regions and data centers. This distribution mitigates risks associated with localized failures.

If one data center goes down, data and services can continue to be served from another location, ensuring uninterrupted service.

Benefits of Regional Data Distribution:

Redundancy: Having multiple copies of data across regions provides redundancy, which is critical for disaster recovery.

Load Balancing: Traffic can be distributed across data centers to optimize performance and reduce latency.

Regulatory Compliance: Storing data in different regions helps meet local data residency requirements.

Implementation in Salesforce Data Cloud:

Salesforce utilizes a robust architecture involving data replication and failover mechanisms to maintain data integrity and availability.

This architecture ensures that even in the event of a regional outage, customer data remains secure and accessible.

Answer : A, B

Introduction to Visual Insights Builder:

The Visual Insights Builder in Salesforce Data Cloud is a tool used to create calculated insights, which are custom metrics derived from the existing data.

Requirements for Creating Calculated Insights:

Measure: A measure is a quantitative value that you want to analyze, such as revenue, number of purchases, or total time spent on a platform.

Dimension: A dimension is a qualitative attribute that you use to categorize or filter the measures, such as date, region, or customer segment.

Steps to Create a Calculated Insight:

Navigate to the Visual Insights Builder within Salesforce Data Cloud.

Select 'Create New Insight' and choose the dataset.

Add at least one measure: This could be any metric you want to analyze, such as 'Total Sales.'

Add at least one dimension: This helps to break down the measure, such as 'Sales by Region.'

Practical Application:

Example: To create an insight on 'Average Purchase Value by Region,' you would need:

A measure: Total Purchase Value.

A dimension: Customer Region.

This allows for actionable insights, such as identifying high-performing regions.

Answer : A

The field typeTextshould be chosen to ensure that leading zeros in the purchase order number are preserved. This is because text fields store alphanumeric characters as strings, and do not remove any leading or trailing characters.On the other hand, number, decimal, and serial fields store numeric values as numbers, and automatically remove any leading zeros when displaying or exporting the data123. Therefore, text fields are more suitable for storing data that needs to retain its original format, such as purchase order numbers, zip codes, phone numbers, etc.Reference:

Zeros at the start of a field appear to be omitted in Data Exports

Keep First '0' When Importing a CSV File

Import and export address fields that begin with a zero or contain a plus symbol

Answer : C

Streaming insight is a method that allows you to perform aggregations in windows of 15 minutes on data collected via the Interaction SDK or Mobile SDK. Streaming insight is a feature that enables you to create real-time metrics and insights based on streaming data from various sources, such as web, mobile, or IoT devices. Streaming insight allows you to define aggregation rules, such as count, sum, average, min, max, or percentile, and apply them to streaming data in time windows of 15 minutes. For example, you can use streaming insight to calculate the number of visitors, the average session duration, or the conversion rate for your website or app in 15-minute intervals. Streaming insight also allows you to visualize and explore the aggregated data in dashboards, charts, or tables.Reference:Streaming Insight,Create Streaming Insights

Answer : A, D

To package Data Cloud components from one organization to another, the consultant should include the following components in a data kit:

Data model objects: These are the custom objects that define the data model for Data Cloud, such as Individual, Segment, Activity, etc.They store the data ingested from various sources and enable the creation of unified profiles and segments1.

Identity resolution rulesets: These are the rules that determine how data from different sources are matched and merged to create unified profiles.They specify the criteria, logic, and priority for identity resolution2.Reference:

1: Data Model Objects in Data Cloud

2: Identity Resolution Rulesets in Data Cloud

Answer : A, D

To address this new use case, the consultant should map the primary key from the two systems to Party Identification, using CRM ID as the identification name for both, and create a matching rule based on party identification that matches on CRM ID as the party identification name. This way, the consultant can ensure that the CRM Contacts and Marketing Contacts are matched based on their CRM ID, which is a unique identifier for each individual. By using Party Identification, the consultant can also leverage the benefits of this attribute, such as being able to match across different entities and sources, and being able to handle multiple values for the same individual. The other options are incorrect because they either do not use the CRM ID as the primary key, or they do not use Party Identification as the attribute type.Reference:Configure Identity Resolution Rulesets,Identity Resolution Match Rules,Data Cloud Identity Resolution Ruleset,Data Cloud Identity Resolution Config Input

Answer : B

Batch transforms are a feature that allows creating new data lake objects based on existing data lake objects and applying transformations on them. This can be useful for splitting, merging, or reshaping data to fit the data model or business requirements. In this case, the consultant can use batch transforms to create a second data lake object that contains only the airline points from the original loyalty data object. The original object can be modified to contain only the hotel points. This way, the client can have two separate records for each point system and track and process them accordingly.Reference:Batch Transforms,Create a Batch Transform