Explanation:

✅ Correct Answer: A. Use Case Assignment rules to check for keywords in the subject or description and assign the case to a specialist queue that is appropriate for each keyword.
Assignment Rules are well-suited for routing cases based on field values like Subject or Description. They allow the system to dynamically analyze the text and route cases to the appropriate team, reducing manual intervention and improving first-response times. This ensures customers reach the right department immediately, without unnecessary transfers or delays.

❌ B. Use a record-triggered flow to detect keywords and assign the case to a specialist queue that matches the keyword.
Although this is technically viable, it adds unnecessary complexity compared to using native Assignment Rules. Unless highly customized logic is needed, flows may be harder to maintain and could introduce latency if not optimized.

❌ C. Add the Type field to the assigned Global Action as required, and then use a record-triggered flow to assign the case to a specialist queue that is appropriate for each.
This adds another layer of user dependency (requiring the user to select a Type), which could introduce inconsistencies. It also overcomplicates the routing logic when keyword-based assignment could be automated more effectively and reliably.

Explanation:

✅ Correct Answer:

D. All of the above, working together to prevent duplicate records and ensure data integrity after migration.
Salesforce offers a multi-layered approach to managing and preventing duplicate records—especially critical during data migration from legacy systems. Matching Rules define how Salesforce identifies duplicate records based on fields like name, email, or phone number. Data Import Wizard offers built-in duplicate prevention options, allowing users to skip, update, or add records based on duplication criteria. Additionally, combining these with Workflow Rules or automation logic provides proactive management by flagging or even automatically resolving duplicates post-import. Together, these tools provide a comprehensive solution to maintaining clean and accurate data throughout and after the migration process, ensuring data integrity and a seamless user experience.

❌ Incorrect Answers:

🔻 A. Matching rules defining criteria for identifying and merging duplicate records
Matching Rules are fundamental, but they only identify duplicates; they don't prevent or resolve them on their own. They're often used in tandem with Duplicate Rules, which specify what actions to take when a match is found (block, allow, or alert). So while this is a necessary component, it's not a complete solution by itself during data migration.

🔻 B. Data Import Wizard with duplicate prevention settings during bulk data importing
The Data Import Wizard does provide duplicate prevention, but it's limited to specific standard objects and fields. It works best for simple imports and offers basic duplicate checking (like based on email or record ID), but lacks the depth and flexibility needed for complex or large-scale migrations. Also, it doesn’t detect nuanced duplicates unless properly configured with matching rules.

🔻 C. Workflow Rules automatically triggering deduplication logic based on specific data fields
Workflow Rules are designed for automation, but they don’t directly identify or merge duplicate records. They can be used to trigger alerts or tasks after a duplicate is suspected, but they need support from other tools like Matching Rules and Flows. On their own, Workflow Rules aren’t sufficient for duplicate prevention during data migration.

Explanation:

When a customer wants to seamlessly integrate their existing CRM system with Salesforce, the goal is to ensure data synchronization, real-time communication, and low maintenance overhead.

Partner Integrations refer to pre-built or certified solutions available via Salesforce AppExchange or third-party providers, which are specifically designed to integrate Salesforce with other CRM platforms or business systems (e.g., Microsoft Dynamics, SAP, HubSpot).

✅ Why D. Partner Integrations is the Best Answer:

➤ Offers plug-and-play integration solutions for common CRM platforms.
➤ Reduces development time and cost.
➤ Ensures ongoing support, compatibility, and best practices.
➤ Maintains data integrity and system performance.
➤ Examples: MuleSoft (Salesforce-owned), Zapier, Jitterbit, Workato, etc.

Explanation:

When unexpected data inconsistencies occur during a Salesforce data migration, the most effective way to preserve data integrity is to pause the process, analyze the root cause, and take corrective action. While other options may offer temporary relief or speed, they risk introducing bad data into your new system—leading to long-term issues in reporting, automation, and customer experience.

🅰️ Option A: Ignoring minor inconsistencies to avoid delaying the migration timeline
While the pressure to meet tight deadlines can be intense, ignoring inconsistencies, even if they seem minor, is generally a poor strategy. Small errors can quickly scale into larger problems once data is live in Salesforce. For example, mismatched picklist values, formatting errors, or null fields could lead to broken reports, faulty automation, or user confusion.

By allowing flawed data into production, you risk eroding user trust, introducing compliance issues, and causing downstream process failures. Short-term speed sacrifices long-term data reliability, which is often more costly to fix post-launch than during migration.

🅱️ Option B: Implementing data cleansing scripts or manual data correction within Salesforce
Cleansing data during or after migration—either via scripts, tools, or manual updates—is a proactive step in maintaining quality. This could involve normalizing values, removing duplicates, or validating references. However, doing so within Salesforce post-migration can be reactive rather than preventative, especially if the issue originated in the source system or mapping logic.

Manual fixes can also be time-consuming and inconsistent without standardized rules. While this approach can work in some cases, especially for small datasets, it doesn’t address why the errors occurred and risks repeating the same issues in future loads.

✅ 🅲 Option C: Pausing the migration, investigating the inconsistencies, and fixing the source data or mapping errors
(Correct Answer)

This is the most effective and responsible strategy. By pausing the migration and thoroughly analyzing the inconsistencies, you can pinpoint whether the problem lies in the source data, the transformation logic, or the field mapping. Once identified, the root issue can be fixed—either in the legacy system, in the staging files, or within the data mapping configuration—before continuing with the migration.

This method ensures clean, consistent data is loaded into Salesforce, which supports downstream functions like reporting, automation, and analytics. It may extend the timeline slightly but results in a higher-quality, trustworthy system that users can rely on from day one.

🅳 Option D: All of the above, depending on the severity and impact of the data inconsistencies encountered
While this answer acknowledges flexibility, it risks justifying poor data practices. Not all inconsistencies are equal, but a blanket approach that includes ignoring errors (Option A) is risky. Data integrity should always be prioritized, and option C remains the best general practice, regardless of severity, because it promotes investigation and resolution rather than workaround behavior.

🧩 Summary:
Maintaining data integrity is essential for the success of any Salesforce implementation. Option C ensures that your migration process is not only fast but also accurate, clean, and scalable. Addressing issues at the root before continuing the migration minimizes risk and sets the foundation for reliable business operations.

📚 Official Salesforce Reference:
🔗 Salesforce Data Quality Best Practices

Explanation:

✅ B. Salesforce Surveys allow you to collect feedback directly from customers once a case is closed. These surveys can be customized or used with built-in templates for common service metrics like CSAT or NPS. They're native to Salesforce, meaning no external integration is required, and responses can be mapped directly into records for real-time reporting. This built-in approach is scalable, secure, and efficient for contact centers looking to track post-interaction satisfaction.

🔴 A. Integrate with a third-party survey platform provides more customization and possibly better analytics, but adds complexity and cost. It often requires API integrations, custom mappings, and potential security reviews, making it less efficient unless you have highly specific survey needs not met by Salesforce Surveys.

🔴 C. Implement chatbots with in-conversation surveys is useful for immediate feedback during the conversation, but it limits feedback to users interacting through chat channels only. It doesn’t work well for cases resolved via phone, email, or other non-chat channels.

🔴 D. Develop custom case fields and workflows is a very manual method. While it can capture satisfaction indicators, it lacks the scalability, user experience, and detailed reporting capabilities of a formal survey system.