SF-Data-Arch Exam Questions and Answers

Use the Bulk API 2.0 with CSV files.

Bulk API 2.0 is purpose-built for high-volume data movement, handling large batches asynchronously to maintain system stability. It provides significant performance gains by allowing Salesforce to manage job batching internally. This minimizes the risk of hitting governor limits and ensures successful large-scale data migrations for enterprise environments.

Implement the Bulk DML Pattern using collections.

Moving DML operations outside of loops is the fundamental requirement for Salesforce Apex development. By collecting records in a List and performing a single DML call, you stay within the 150 limit per transaction. This is a critical architectural pattern for ensuring scalable code during heavy data ingestion processes.

Implementing standard and custom Matching Rules.

Matching rules are essential for identifying potential duplicates during record creation. By defining specific criteria based on business logic, Salesforce can flag or block duplicates automatically, ensuring that records remain unique and that the database integrity is maintained across different user input sources and automated integration processes.

Establishing a Unique Identifier (External ID) strategy.

Using External IDs from source systems is a cornerstone of MDM. It allows Salesforce to reliably map records from disparate systems, preventing the accidental creation of duplicate records during synchronization. This provides a clear link between systems, facilitating data consistency and simplifying the integration architecture for enterprise data flows.

Deploying a centralized Data Governance policy.

Data governance defines the standards, roles, and responsibilities for managing data. Without a clear policy, different departments may input data in conflicting formats, leading to duplicates that matching rules might miss. Governance ensures that all stakeholders understand the importance of quality, consistency, and the processes for resolving conflicting data.

Salesforce Data Loader.

Data Loader is the robust industry standard for handling large, complex data imports and exports. It supports relational mapping, allowing architects to preserve parent-child hierarchies during migration. Its logging and error handling capabilities are essential for auditing and ensuring that data is correctly inserted into the Salesforce environment.

It enables efficient Upsert operations.

The Upsert operation relies on External IDs to identify if a record exists. By making the field unique and an External ID, the system can determine whether to create a new record or update an existing one. This is the foundation of efficient, idempotent data synchronization in enterprise integrations.

Use Field Audit Trail (Shield).

Field Audit Trail provides the deep, long-term history tracking required for compliance. It supports up to 10 years of data and allows for tracking a significantly higher number of fields than standard history tracking. It is the architecturally correct choice for meeting stringent regulatory data retention and audit requirements.

Create a summary object to store pre-aggregated data.

Pre-aggregation shifts the computational load from read-time to write-time. By calculating metrics asynchronously and storing them in a summary object, the report engine only processes a small number of aggregate records rather than scanning the entire base table, significantly reducing execution time and resource consumption.

Disable unnecessary triggers during the data load.

Triggers run for every record processed, consuming CPU and database resources while creating row locks on parent or related objects. Disabling them allows the system to focus exclusively on record insertion, significantly increasing throughput and avoiding potential deadlocks caused by concurrent logic execution during the migration.

Reduce the number of indexes on the target object.

Every index requires an update during the record insertion process. By minimizing the number of indexes, the database engine spends less time updating metadata and more time writing the actual data. This reduction is a critical best practice for accelerating high-volume data loads into Salesforce objects.

The field 'Status__c' is not indexed.

Without an index on the 'Status__c' field, the database must perform a full table scan to evaluate every record. Since the object contains millions of records, this exceeds the system's threshold for selectivity, causing the query to fail to protect overall system performance and availability.

Salesforce Connect.

Salesforce Connect integrates external data into the org via external objects. This allows companies to keep data outside of the Salesforce database, preventing native performance degradation while ensuring users still have access to the information, which is a perfect pattern for large-scale data archival and management needs.

Use an asynchronous approach to aggregate parent updates.

Asynchronous processing allows updates to be queued and batched, preventing multiple concurrent transactions from fighting over the same parent row. This pattern is essential for high-frequency updates, as it serializes the parent-level changes and allows the system to process them efficiently without risking transactional row locks.

Sort the input data by the lookup field values.

Sorting the input file by the parent ID ensures that all children associated with the same parent are grouped together. This serializes access to the parent records, preventing multiple batch threads from attempting to lock the same parent concurrently, which is the primary cause of row contention.

Disable unnecessary triggers on the parent objects.

Triggers on parent objects often perform calculations or updates that hold locks on the parent record. By disabling these during large loads, you remove extra processing time and lock duration, allowing the bulk operation to complete faster and reducing the probability of triggering locking errors.

Define a formal Data Classification Policy.

A formal classification policy establishes the metadata tagging and handling rules necessary for governance. It provides a source of truth for technical teams to configure security features accurately. Without this framework, organizations face inconsistent security postures and difficulty proving compliance during audits, as there is no standardized data categorization schema.

Identify and document data quality issues.

Data stewards actively monitor data sets to identify inconsistencies, duplicates, or missing information. By logging and documenting these issues, they provide the necessary insight for IT to refine validation rules or automation processes. This proactive documentation is essential for maintaining high trust in the organization's data assets.

Manage metadata and business glossary entries.

Stewards ensure that fields are well-defined and understood by the business. By managing the business glossary and metadata, they facilitate consistent data usage across different departments. This prevents misinterpretation of data fields and ensures that reports and dashboards are built on a common, agreed-upon understanding of business logic.

Inconsistent enforcement of privacy compliance.

When documentation deviates from actual Salesforce security settings, compliance audits will fail. The system becomes vulnerable because the policy exists in a silo, and the technical implementation fails to match the required controls. This inconsistency creates a false sense of security, exposing the organization to legal and regulatory penalties.

Data Inventory and Mapping.

A data inventory maps the flow and storage location of data across the enterprise. It is essential for residency compliance because it identifies where data originates, moves, and resides. Without this, an organization cannot prove to regulators that they are keeping data within the required legal jurisdictions or borders.

Resolving data ownership and stewardship conflicts.

Conflicts often arise regarding who 'owns' a piece of data, leading to inconsistent definitions. The council acts as the final arbiter for these disputes, ensuring that roles are clearly defined and that business units agree on the sources of truth, which is essential for uniform reporting and data usage.

Setting organization-wide data standards.

Standardization is crucial for interoperability between systems. The council defines naming conventions, data formats, and quality metrics that apply across all business units. This uniformity reduces integration costs and ensures that data analyzed in one system is comparable to data analyzed in another, creating a reliable enterprise-wide view.

Fostering data stewardship and accountability.

Accountability is the backbone of governance. By institutionalizing the role of the data steward, the council ensures that someone is always responsible for the data's health. This culture of ownership prevents the 'tragedy of the commons' where no one takes responsibility for data quality, eventually leading to massive degradation.

Federated Governance.

Federated governance provides a scalable framework that combines central standards with localized execution. It empowers business units to manage their specific domains while staying within an enterprise-aligned policy framework. This flexibility is essential for large organizations to maintain consistency without hindering the speed of business operations and local innovation.

Implement a centralized MDM hub to mediate data exchanges and manage the golden record.

A dedicated MDM hub provides the necessary governance and orchestration layer to manage data survivorship, merging, and validation. By acting as the authoritative source, the hub ensures consistent data quality before distributing updates to Salesforce and SAP, effectively resolving conflicts and maintaining a unified golden record.

Define deterministic and probabilistic matching rules.

Deterministic matching uses exact identifiers like email or ID, while probabilistic matching uses fuzzy logic to link records based on confidence scores. Combining both approaches maximizes the reach of the identity resolution process, ensuring that fragmented customer interactions are accurately linked into a single cohesive profile.

Establish survivorship rules to determine which system's data takes precedence.

Survivorship rules are essential when multiple systems provide conflicting information for the same attribute. These rules define which source is the most trusted for specific fields, ensuring the unified profile reflects the most accurate and current information while preventing stale data from overwriting high-quality records.

Managing data quality, including defining rules and resolving data conflicts.

Data stewards are responsible for establishing the quality standards and resolving disputes that automated systems cannot handle. By managing the definitions and policies governing master data, they ensure that the organization maintains a clean, reliable, and trusted data foundation that supports informed business decision-making and efficient operations.

It identifies patterns, anomalies, and quality issues within the legacy data set.

Profiling tools analyze columns to discover frequency distributions, null counts, and data format variances. This insight allows architects to create accurate data cleansing logic, ensuring that the migrated data is clean, standardized, and ready for use in Salesforce, which is vital for maintaining the integrity of the MDM strategy.

Assign data owners for each critical data domain.

Data ownership establishes accountability. By assigning owners, the organization ensures there is a clear point of contact for defining data standards, resolving disputes, and maintaining the quality of specific domains, which prevents the 'tragedy of the commons' where no one takes responsibility for data maintenance.

Implement automated validation rules on all critical fields.

Automated validation enforces standards at the point of entry. By preventing invalid data from being saved, the system ensures that only high-quality data enters the CRM, reducing the long-term burden on data stewards to perform manual cleanup and improving the reliability of reports and dashboards.

Document and publish clear data definitions and standard operating procedures.

Clear documentation provides a common language and set of rules for all users. By publishing definitions and procedures, the organization ensures that data is entered and interpreted consistently across all departments, reducing ambiguity and preventing the creation of duplicate records caused by differing interpretations of business data.

They determine which data source is trusted for specific fields during reconciliation.

Survivorship logic allows architects to assign 'trust scores' or hierarchies to data sources. If the ERP is deemed the source of truth for financial data, the survivorship rule ensures that incoming ERP data overrides any Salesforce entries for those fields, effectively maintaining data accuracy in the master record.

Use a Custom Setting to control trigger execution.

A Custom Setting (or Custom Metadata) provides a clean, highly performant way to toggle Apex execution logic. This allows developers to disable resource-intensive triggers temporarily for the duration of the data load and reactivate them immediately afterward without needing a full code deployment cycle.

The total number of records to be migrated.

The volume of records is the deciding factor. Bulk API 2.0 is purpose-built for high-volume jobs, handling large batches asynchronously to optimize server resources. In contrast, the SOAP API is designed for smaller, real-time integrations, and attempting to use it for large migrations causes timeouts and system stress.

Perform data masking on source datasets for sandboxes.

Data masking is a mandatory security practice for non-production environments to prevent the exposure of PII. By sanitizing the data before it reaches the sandbox, the risk of data leakage is minimized while still allowing developers to test against realistic, but safe, dataset structures.

Enable Field-Level Security to restrict access to sensitive fields.

Field-Level Security is a foundational platform control that restricts visibility of PII to only those users who require access for their roles. This adheres to the principle of least privilege, ensuring that even if data is successfully migrated, it remains secure from unauthorized internal viewing.

It leads to record locking contention on parent objects.

Parallel processing attempts to update records as fast as possible. If multiple threads attempt to update or insert child records that share the same parent, they will contend for the parent record lock, resulting in 'UNABLE_TO_LOCK_ROW' errors that can stop the migration process.

Handling duplicate record IDs across source orgs.

Since Salesforce IDs are only unique within a single org, consolidation will lead to ID collisions. Architects must map original records to new unique external keys and cross-reference them to ensure that parent-child relationships remain intact within the new, consolidated environment after the data is migrated.

Merging disparate security models and profiles.

Different orgs often have completely different security structures. Consolidating requires mapping roles, profiles, and sharing rules into a unified model. Failure to align these models leads to data being exposed to users who should not see it, or users losing access to data they previously owned.

Mapping custom fields with identical names but different data types.

Even if fields share the same label, their underlying data types or picklist values may differ across orgs. A rigorous metadata gap analysis is required to normalize these discrepancies into a single, unified schema before any actual record data can be migrated successfully.

Accounts, to establish the parent records for children.

Accounts serve as the foundation of the relational data model. By loading them first, you generate the Salesforce IDs necessary to map children (Contacts and Opportunities) correctly during subsequent loads. This top-down hierarchical approach is the industry-standard sequence for successful data migration projects in Salesforce.

Master-Detail relationship

Master-Detail relationships enforce a strict ownership model where the child record is dependent on the parent. The relationship field is mandatory, and the system automatically enforces a cascade delete when the parent is deleted, directly satisfying the requirement for mandatory association and lifecycle management.

Use Big Objects

Big Objects allow for the storage and querying of massive datasets without impacting performance or standard data storage limits. They are specifically engineered to handle high volumes, making them the optimal choice for weekly snapshots that need to be queried but not frequently updated or edited.

Security and sharing inheritance

Master-Detail relationships force child records to inherit the sharing and security settings of the parent. In contrast, Lookup relationships allow the child to have its own independent sharing rules, making this a fundamental design decision for data access control and visibility across different user profiles.

Need for Rollup Summary fields

Rollup summary fields are exclusively available on Master-Detail relationships. If the business requirement demands the calculation of aggregate data like counts, sums, or averages from child records on the parent record, a Master-Detail relationship is the only native declarative option to implement this functionality.

External ID field

An External ID field, marked as unique, is designed specifically for this integration use case. It allows the Upsert operation to identify existing records based on the ERP key, ensuring that data is updated correctly rather than creating duplicate entries during every data synchronization cycle.

Set Audit Fields permission

The 'Set Audit Fields' permission is the specific security requirement needed to override system-managed fields. When this is enabled for the integration user, the system allows the ingestion of original creation timestamps and user IDs, ensuring that legacy data maintains its historical integrity after the migration.

Create a custom index

Custom indexes on high-cardinality fields directly address performance issues by allowing the query engine to pinpoint records without a full table scan. This is the optimal way to handle frequently filtered data, ensuring that queries remain performant even as the volume of records in the database grows.