Number of joins used in report query.

Number of records returned by report query.

Explanation:

Universal Containers manages over 200 million opportunities (10 million accounts × 20 opportunities) and needs a daily report for a specific opportunity stage. With such large data volumes, report performance is critical. Key considerations must focus on minimizing processing complexity and data retrieval to ensure fast, efficient reporting, avoiding delays that could frustrate the Sales Executive’s daily workflow.

Correct Option: 🅱️ Number of joins used in report query
Joins in report queries, like those linking Opportunities to Accounts, increase processing time, especially with 200 million opportunities. Minimizing joins by focusing only on necessary fields (e.g., stage) reduces complexity. For example, avoiding unrelated objects in the query keeps the report lean, ensuring faster performance. Salesforce recommends optimizing joins for large data volumes to prevent timeouts or sluggish reports.

Correct Option: 🅲 Number of records returned by report query
The number of records returned directly impacts report performance. With 200 million opportunities, filtering to a specific stage (e.g., “Closed Won”) reduces the result set, speeding up processing. Large result sets strain Salesforce’s resources, causing delays. Using selective filters ensures only relevant data is retrieved, aligning with best practices for efficient reporting in high-volume environments.

Incorrect Option: 🅰️ Number of queries running at a time
The number of concurrent queries isn’t a direct consideration for a single report’s performance. While org-wide query volume can affect overall system performance, the question focuses on the report itself. Factors like joins and record count within the report query have a greater impact, as concurrent queries are managed by Salesforce’s governor limits, not report design.

Incorrect Option: 🅳 Number of characters in report query
The number of characters in a report query doesn’t significantly affect performance. Salesforce processes queries based on data volume and complexity, not character count. For example, a short query with many joins or large record sets will perform worse than a longer, optimized query. This option is irrelevant to ensuring efficient reporting for UC’s large opportunity dataset.

Reference
Salesforce Help: Reports and Dashboards
Salesforce Architect Guide: Large Data Volumes

Determine if any integration points create records in Salesforce.

Run key reports to determine what fields should be required.

Review the sharing model to determine impact on duplicate records.

Explanation:

Summary:
Data stewardship focuses on maintaining quality, trust, and compliance of data. To make meaningful design recommendations, the architect must evaluate integrations that generate data, understand which fields are essential, and consider how sharing models impact duplicates and ownership. Together, these areas highlight weak points in governance and guide improvements without guessing.

Correct Options

✅ B. Determine if any integration points create records in Salesforce.
Integrations can bypass validations or inject bad data if not governed properly. Reviewing these points helps ensure incoming data follows the same rules as user-entered records.

✅ C. Run key reports to determine what fields should be required.
Reports highlight usage patterns—if certain fields are consistently empty, they may not need to be required, while business-critical fields that drive analytics should be mandated. This aligns governance with real usage.

✅ E. Review the sharing model to determine impact on duplicate records.
Sharing rules affect visibility—if users can’t see all data, they may unknowingly create duplicates. Reviewing sharing helps address data quality issues tied to accessibility.

Incorrect Options

❌ A. Review the metadata XML files for redundant fields to consolidate.
While metadata can show field definitions, XML files aren’t a practical way to assess stewardship issues. Field usage is better analyzed through reports and adoption metrics.

❌ D. Export the setup audit trail to review what fields are being used.
The setup audit trail tracks admin configuration changes, not field-level data usage. It doesn’t provide insights into stewardship or data quality concerns.

Reference:
Salesforce Help – Data Governance Best Practices

Use a service to standardize Account addresses, then use a 3rd -party tool to merge Accounts based on rules.

Explanation:

Data quality issues like duplicate Accounts are common in growing Salesforce orgs. The best solution is a two-step approach: first, normalize the data (such as cleaning and standardizing addresses), then apply a tool or process to identify and merge true duplicates. This ensures accuracy, avoids accidental merges, and improves reporting consistency.

Correct Option

✅ A. Use a service to standardize Account addresses, then use a 3rd-party tool to merge Accounts based on rules.
Address normalization is key—“123 Main St.” and “123 Main Street” should match consistently. Once data is standardized, advanced third-party deduplication tools can apply flexible matching rules to safely merge duplicates without losing valuable data.

Incorrect Options

❌ B. Run a report, find Accounts whose name starts with the same five characters, then merge those Accounts.
This is too simplistic and prone to error—“Acme Corp” and “Acme Services” might be different companies, while duplicates may not share the first five letters. It’s not a reliable deduplication method.

❌ C. Enable Account de-duplication by creating matching rules in Salesforce, which will mass merge duplicate Accounts.
Salesforce duplicate management works well for preventing new duplicates but doesn’t offer automated mass merging. This option overstates Salesforce’s native deduplication capabilities.

❌ D. Make the Account Owner clean their Accounts' addresses, then merge Accounts with the same address.
Putting this responsibility on end users is inefficient and inconsistent. Address matching alone also misses many duplicates where addresses differ slightly or aren’t populated.

Reference:
Salesforce Help – Duplicate Management

Identify data elements to be persisted in salesforce.

Map legacy data to salesforce objects.

Work with legacy application owner to analysis legacy data model.

Explanation:

Data modeling during migration isn’t just a lift-and-shift from the old system—it’s about understanding what really belongs in Salesforce and how it should be structured. The architect must carefully evaluate which elements to carry over, how they map to standard/custom objects, and align with business needs by collaborating with legacy app owners. This avoids clutter, reduces technical debt, and ensures the model supports Salesforce best practices.

Correct Options

✅ A. Identify data elements to be persisted in Salesforce.
Not everything from the legacy system should come over. By carefully selecting the key data elements that drive business processes, UC avoids overloading Salesforce with unnecessary or irrelevant data, keeping the org lean and efficient.

✅ B. Map legacy data to Salesforce objects.
Proper mapping ensures legacy data fits naturally into Salesforce’s structure. Whenever possible, standard objects like Account, Contact, and Opportunity should be used before creating custom ones. This makes data easier to maintain and reduces complexity.

✅ D. Work with legacy application owner to analyze legacy data model.
The legacy owner holds critical knowledge of how the system was built and why. Their input ensures the migration captures the right relationships and avoids misinterpretation of how fields or tables were originally used.

Incorrect Options

❌ C. Map legacy data to Salesforce custom objects.
Custom objects should only be created if the data doesn’t logically belong in standard objects. Assuming everything requires a custom object would create redundancy and increase admin overhead unnecessarily.

❌ E. Implement legacy data model within Salesforce using custom fields.
Salesforce data models are not meant to be replicas of old databases. Copying the legacy model field-for-field leads to inefficiency and bloated object structures, missing the opportunity to simplify and align with Salesforce standards.

Reference:
Salesforce Help – Data Modeling Best Practices

Leverage on premise data archival and build integration to view archived data.

Leverage big object to archive case data and lightning components to show archived data.

Explanation:

UC faces a storage crisis with millions of Cases. They need to retain data for 7 years for compliance but must free up storage and maintain performance. The solution must provide access to this archived data without burdening the primary Salesforce database.

Correct Options:

C. 🗄️ Leverage on-premise data archival and build integration to view archived data.
D. 🗃️ Leverage Big Objects to archive case data and Lightning components to show archived data.

Both C and D are correct. Option C involves archiving to an external system (on-premise or cloud) and building a custom integration (e.g., using APIs or a mashup) for access. Option D uses Salesforce Big Objects, a native archive solution designed for massive, rarely accessed data that can be queried via SOQL and displayed in custom Lightning components. Both effectively remove data from the primary Case table to free up storage.

Incorrect Options:

A. 📄 Export data out of Salesforce and store in flat files on an external system. While this archives data, flat files are not a suitable solution for user access. They cannot be easily queried or displayed within Salesforce for user groups, violating the access requirement. This is only a pure backup.

B. 🧱 Create a custom object to store case history and run reports on it. This does not solve the storage issue; it merely moves the data from one Salesforce object to another, continuing to consume expensive primary data storage. It is an internal relocation, not an archive.

Reference:
Salesforce Help: Big Objects
Salesforce Help: Data Archiving Considerations