SF-Data-Arch Practice Test 2 — 10 Questions

Question 1

When designing a master data management solution in Salesforce, what is the significance of the 'Survivorship' rules?

Accepted Answer

They determine which data source is trusted for specific fields during reconciliation.. Survivorship rules are the logic engine that determines which source provides the 'best' version of a record when multiple systems provide conflicting data. This is essential for MDM because without these rules, the system cannot intelligently reconcile discrepancies, leading to a fragmented customer view. Proper survivorship logic ensures the golden record is reliable, accurate, and reflects the most trusted information available across the entire enterprise ecosystem.

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They define the order in which records should be archived to the recycle bin.

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They enable the automatic encryption of sensitive fields in the database.

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They force users to enter data in a specific order to prevent errors.

Question 2

Universal Containers uses a Big Object named 'Log__b' to store audit trails. They need to generate a report that aggregates this data weekly for compliance. Standard SOQL queries are failing due to the volume of records. What should the architect recommend?

Accepted Answer

Use CRM Analytics (formerly Tableau CRM) to ingest and aggregate the data.. Big Objects are optimized for massive scale but do not support standard SOQL features like aggregate functions or complex filtering over large sets. Async SOQL was the historical solution, but it has been retired. The modern recommendation is to use CRM Analytics or external reporting tools to process Big Object data.

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Use a Skinny Table on the Big Object to improve query performance.

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Create a Summary Report in Salesforce and schedule it for weekly delivery.

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Write an Apex batch job to query the Big Object using the 'GROUP BY' clause.

Question 3

A large custom object has 20 million records. A SOQL query is taking too long. What should the architect evaluate first?

Accepted Answer

Verify if the query filters are selective and indexed.. The architect should first inspect the query's filter criteria to determine if it is selective. If the filter is not indexed or is too broad, the system will perform a full table scan. Verifying the selectivity of the query and ensuring an index exists is the fundamental first step in optimizing performance for large data volumes within the Salesforce platform.

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Check if the object has too many fields.

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Increase the organization's total storage limit.

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Rewrite the query to use the Data Loader API.

Question 4

Which indexing strategy is best to improve performance for queries on a field used in multiple filters?

Accepted Answer

Create a custom index. Custom indexes are necessary when standard indexing is insufficient for complex filtering. By creating a custom index on high-cardinality fields used in 'WHERE' clauses, the optimizer can skip full table scans, drastically reducing query time. This is critical in large data volumes where even a small performance improvement in query speed can make the difference between a successful report and a timeout failure.

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Use a formula field

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Use a text area field

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Use a standard lookup

Question 5

A firm is implementing a data stewardship program. Which TWO activities are primary responsibilities of a Data Steward? (Choose Two)

Accepted Answer

Identify and document data quality issues.. Data stewards bridge the gap between technical IT teams and business users. They are responsible for the daily management, quality, and definition of data assets. By ensuring metadata is documented and data quality issues are remediated, they maintain the integrity of the data assets, which is critical for trustworthy analytics and regulatory reporting within the Salesforce ecosystem.

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Define the overarching corporate data strategy.

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Design the technical Salesforce architecture.

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Approve all system-level security access.

Question 6

What is the recommended approach to manage 'Skinny Tables' in an environment with Large Data Volumes?

Accepted Answer

Request them only for high-read-volume objects where performance is critical.. Skinny tables are a specialized feature used to improve performance by flattening data structures to avoid joins. They should only be used when standard indexing is insufficient for performance. Because they require Salesforce Support to maintain and are automatically synchronized, they are a powerful but rigid tool that should be applied sparingly to the most critical, high-read-volume scenarios only, to avoid unnecessary maintenance overhead.

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Enable skinny tables for every object with more than 1 million records.

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Manually update skinny tables using DML statements in Apex.

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Always include every field of the object in the skinny table.

Question 7

Universal Containers is implementing a data retention strategy for a custom object called Invoice__c that stores 120 million records. They need to archive records older than seven years to an external system while keeping the most recent records immediately accessible in Salesforce. Compliance requires that archived records remain queryable for audit purposes without impacting Salesforce performance. Which two Salesforce features should the architect recommend to meet these requirements? (Choose two.)

Accepted Answer

Big Objects with a custom index and asynchronous SOQL queries. Big Objects provide native, scalable storage for massive data volumes and support asynchronous queries, making them ideal for long-term archival within Salesforce. Salesforce Connect allows real-time access to external data without replication, so archived records remain queryable for audits. Together, they satisfy retention, performance, and compliance requirements without overloading the transactional object.

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Standard Salesforce Recycle Bin retention policies

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Field Audit Trail and Data Retention policies for standard objects

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Salesforce Data Loader with a scheduled export to a local database

Question 8

A Data Architect is designing a data governance strategy for a Salesforce org that integrates with multiple external systems. The architect needs to ensure data quality and consistency across systems. Which two practices should be implemented? (Choose two.)

Accepted Answer

Define a master data management (MDM) strategy to establish a single source of truth.. The two correct practices are defining an MDM strategy and implementing validation and duplicate rules. MDM establishes a single source of truth, while validation and duplicate rules enforce data quality at the point of entry. Together, they ensure data consistency and accuracy across integrated systems, which is essential for effective data governance.

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Schedule nightly full data exports to a data warehouse for backup purposes.

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Configure field-level security to restrict access to sensitive fields.

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Use Salesforce Data Loader to manually reconcile data discrepancies on a weekly basis.

Question 9

A global organization requires that sensitive customer data be categorized, protected, and auditable across Salesforce Orgs. Which data governance framework component is most effective for ensuring consistent data classification policies?

Accepted Answer

Define a formal Data Classification Policy.. Establishing a formal Data Classification Policy is the foundational step for governance. It defines the sensitivity levels and handling requirements for data elements. By standardizing these definitions, architects ensure that technical controls like Shield Platform Encryption or Field Level Security are applied consistently across distributed environments, minimizing risk of unauthorized access and ensuring compliance with global data privacy regulations.

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Enable Shield Platform Encryption for all fields.

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Implement Field Level Security on all PII fields.

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Automate data purging using Apex triggers.

Question 10

A Salesforce org has a custom object Shipment__c with 12 million records. Users frequently run a SOQL query that filters on Status__c and Order_Date__c, and sorts by CreatedDate. The query is timing out. The org has an index on Status__c and a custom index on Order_Date__c. What is the most likely reason the query is still slow?

Accepted Answer

The query is non-selective because the combination of filters does not reduce the result set below the selectivity threshold, and sorting by CreatedDate requires additional processing.. For a 12-million-record object, selectivity is the critical factor. Salesforce uses a threshold based on object size to decide whether to use an index. Even with indexes on Status__c and Order_Date__c, if the filters return too many rows, the optimizer performs a full scan. Sorting by CreatedDate adds overhead. The correct diagnosis is that the query is non-selective and the sort compounds the cost.

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The query is slow because it sorts by CreatedDate, which is not indexed, and Salesforce cannot sort large result sets without an index on the sort field.

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The custom index on Order_Date__c is not used because Salesforce does not support custom indexes on date fields.

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The query is slow because Status__c is a picklist field, and picklist fields cannot be indexed.