Data duplication

Data accuracy and quality

Data standardization

Explanation:

A Master Data Management (MDM) solution is designed to create a single, authoritative source of truth for an organization's critical data, such as customer information. The primary issues described (data duplicated across systems, not in sync, and customer complaints about repeated emails) are classic symptoms of a lack of MDM.

✅ C. Data duplication:
This is the core problem MDM solves. It identifies and merges duplicate records across various systems (e.g., Service Cloud, Marketing Cloud, Data Warehouse) to create a single, golden record.

✅ D. Data accuracy and quality:
By centralizing and managing customer data in a single system, an MDM tool ensures that information is accurate and consistent across the enterprise. It provides a governed process for updates, preventing conflicting information from being stored in different places.

✅ E. Data standardization:
MDM enforces consistent data formats, values, and rules across all systems. For example, it can standardize address formats or naming conventions, which is critical for clean, usable data. The problem of customers having to call in to update their information in multiple places is a symptom of poor standardization.

❌ A. Data completeness:
While an MDM solution can help with completeness by merging data from various sources, it is not its primary function. Its main purpose is to manage the "master" data, not to ensure every field is filled out, which is typically a data governance and business process issue.

❌ B. Data loss and recovery:
This is incorrect. Data loss and recovery are functions of backup and disaster recovery systems, not MDM. MDM focuses on managing data consistency and quality, not on recovering data from a system failure.

Explanation:

Cloud Kicks needs a solution to send data from Salesforce to an external Order Management System (OMS) for invoice generation, with the ability for administrators to customize the fields sent without modifying code. Let’s evaluate each option:

Option A: A set to determine which fields to send in an HTTP callout
Salesforce does not have a native feature called a “set” for configuring fields in HTTP callouts. This option is likely referring to a generic or custom metadata solution, but it is not a standard Salesforce construct. Without a specific feature like Field Sets or Outbound Messages, this approach is not viable for administrators to customize fields without code changes.

Option B: An Outbound Message to determine which fields to send to the OMS
Outbound Messages are a workflow action in Salesforce that allows administrators to send SOAP-based messages to external systems, including selected object fields, without writing code. Administrators can configure Outbound Messages through the Salesforce UI (via Workflow Rules or Flow) and choose which fields to include in the message payload. This meets the requirement for a no-code solution that allows field customization for integration with the OMS.

Option C: A Field Set that determines which fields to send in an HTTP callout
Field Sets are a Salesforce feature that allows administrators to define a collection of fields on an object that can be referenced in code (e.g., Apex or Flow) or Visualforce pages. Developers can write an HTTP callout (e.g., using Apex) that dynamically retrieves fields from a Field Set, enabling administrators to modify the fields sent to the OMS via the Salesforce UI without code changes. This is a flexible, no-code administration solution that meets the requirements.

Option D: Enable the field-level security permissions for the fields to send
Field-level security (FLS) controls which fields users can view or edit within Salesforce, but it does not provide a mechanism for selecting fields to send to an external system via integration. FLS is unrelated to the requirement of customizing fields for an external system without code.

Why B and C are Optimal:
Outbound Messages (B) allow administrators to configure field selections for SOAP-based integrations without coding, directly addressing the requirement for no-code customization. Field Sets (C) enable administrators to define and modify fields for HTTP callouts, providing flexibility in a REST-based integration scenario when paired with minimal Apex or Flow logic. Both approaches empower administrators to customize fields sent to the OMS without requiring developer intervention.

References:
Salesforce Documentation: Outbound Messages
Salesforce Documentation: Field Sets
Salesforce Architect Guide: Integration Patterns

Explanation:

Defining data quality standards for a complex Salesforce org with issues like incomplete and duplicate data requires focusing on metrics and rules that directly address data integrity, usability, and reliability. Let’s analyze each option to identify the three key factors:

✅ Option A: Define data duplication standards and rules
This is a critical factor. Duplicate data is a common issue in Salesforce orgs and directly impacts user experience, reporting accuracy, and system performance. Defining standards and rules for identifying, preventing, and resolving duplicates (e.g., using matching rules, duplicate rules, or third-party tools like Data.com Dupeblocker) is essential for maintaining data quality. This addresses one of NTO’s primary complaints.

Option B: Define key fields in staging database for data cleansing
While a staging database can be useful for data cleansing in migration or integration scenarios, it is not a core component of defining data quality standards within the Salesforce org. Staging databases are typically part of implementation processes, not ongoing data quality management. This option is less relevant to the goal of establishing standards for the existing org’s data issues.

✅ Option C: Measure data timeliness and consistency
Data timeliness (ensuring data is up-to-date) and consistency (ensuring data aligns across objects and systems) are key data quality metrics. For example, outdated records or inconsistent data between related objects (e.g., Accounts and Contacts) can lead to user dissatisfaction and errors in reporting. Measuring these factors helps NTO ensure data remains relevant and reliable, addressing user complaints about data issues.

Option D: Finalize an extract transform load (ETL) tool for data migration
While ETL tools are valuable for data migration or integration, selecting a tool is a tactical decision, not a factor in defining data quality standards. ETL tools may be used to implement data quality processes, but the question focuses on defining standards, not choosing tools.

✅ Option E: Measure data completeness and accuracy
This is another critical factor. Incomplete data (e.g., missing required fields) and inaccurate data (e.g., incorrect values) are explicitly mentioned as issues by NTO’s users. Measuring completeness (ensuring all necessary fields are populated) and accuracy (ensuring data reflects reality) is fundamental to establishing data quality standards and improving user trust in the system.

✅ Why A, C, and E are Optimal:
These three factors directly address NTO’s data quality issues (incomplete and duplicate data) and align with standard data quality frameworks. Defining duplication standards (A) tackles duplicate records, measuring timeliness and consistency (C) ensures data is current and coherent, and measuring completeness and accuracy (E) addresses missing or incorrect data. Together, these form a comprehensive approach to defining data quality standards for the Salesforce org.

References:
Salesforce Documentation: Duplicate Management
Salesforce Architect Guide: Data Quality
Salesforce Help: Data Quality Best Practices

Person Accounts

Explanation:

A data architect at a large B2C retailer needs to model the consumer account structure in Salesforce. B2C businesses primarily interact with individual consumers, not companies. The standard Salesforce data model separates Accounts (companies) and Contacts (people), which is ideal for B2B. For B2C, a different approach is needed to represent a single person as both the customer and the contact.

Correct Option

✅ D. Person Accounts
Person Accounts are a special type of account designed specifically for the B2C (Business-to-Consumer) model. A Person Account combines the attributes of a standard Account and a Contact into a single record, making it the perfect solution for modeling individual consumers. This unified record simplifies data management, streamlines reporting, and provides a clear, single view of the customer, which is essential for a B2C business where the "Account" is the "Person."

Incorrect Options

❌ A. Individual Accounts
Individual Accounts are not a standard Salesforce feature; they are a custom naming convention or record type often used in conjunction with a B2B model, where each "Account" represents a person rather than a company. This approach can lead to data integrity issues and is not the native, supported way to handle B2C relationships. Person Accounts are the standard Salesforce feature for this use case.

❌ B. Account Contact
"Account Contact" is not a standard feature or a data object in Salesforce. This likely refers to the standard Account-Contact relationship, where a Contact is a person associated with a business Account. This model is designed for B2B scenarios and is not suitable for a B2C retailer, where the individual consumer is the core of the business relationship.

❌ C. Contacts
While Contacts represent people, they cannot exist in Salesforce without being related to an Account. A B2C retailer could not simply use the Contact object on its own, as it would violate the core data model. Every Contact must have an associated Account, which would force the B2C company to create a dummy account for every single person, leading to a messy and inefficient data structure.

Reference
Salesforce Help Article: When to Use Person Accounts

Explanation:

The correct approach to archiving is to establish a secure, external storage location (such as a data lake, EDW, or archive database) and use scheduled batch jobs to migrate aged Salesforce records there. Once archived, the records can be purged from Salesforce, freeing up storage and improving query performance. Batch jobs are scalable, automatable, and handle high-volume data, making this the best fit for performance and long-term data retention needs.

Why not the others?

B. Use a time-based workflow:
Workflows can trigger actions at specific times, but they aren’t designed for bulk data movement or archiving. They can only update fields, send emails, or create tasks. Moving thousands or millions of records out of Salesforce requires Batch Apex or ETL tools, not workflows. Attempting archiving with workflows is unscalable and unsupported.

C. Use a formula field and export reports to SharePoint:
Formula fields can flag records by age, and reports can export subsets of data. However, this is a manual, inefficient process. It doesn’t provide automated archiving, data integrity checks, or integration with enterprise-grade storage. SharePoint is also not designed for structured Salesforce data archives with relational dependencies.

D. Create a full copy sandbox for archives:
Sandboxes are meant for testing and development, not data archiving. A full copy sandbox replicates production data periodically, but it doesn’t reduce storage in production or improve performance. It also becomes stale quickly, requiring refreshes. Using sandboxes for archiving is expensive, inefficient, and does not meet compliance or retention goals.

Reference:
Salesforce Data Management Best Practices