SF-Data-Arch Exam Simulation — 100 Questions

Question 1

A data architect is migrating 50 million records into a custom object. To ensure optimal performance and avoid hitting governor limits, which strategy should be implemented during the initial data load?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Leverage Bulk API 2.0 and disable non-essential automation, triggers, and validation rules.. Utilizing the Bulk API 2.0 with serial mode for initial loads minimizes contention on shared resources and prevents record locking errors during high-volume inserts. Designing around index selectivity and disabling unnecessary automation like workflow rules or triggers before loading is crucial for performance. This approach ensures data integrity while drastically reducing the time required to complete the migration compared to standard synchronous API methods.

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Enable all existing triggers and validation rules to ensure data quality immediately upon insertion.

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Use the SOAP API to perform synchronous inserts for real-time validation feedback.

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Increase the batch size to the maximum allowed limit for standard REST API calls.

Question 2

An architect is designing a governance framework for Master Data Management (MDM). Which approach is best for ensuring 'Golden Record' consistency in Salesforce?

Accepted Answer

Implementing an external MDM hub.. A Master Data Management strategy requires a 'Source of Truth' identification. By defining which system holds the primary record and using integration middleware to synchronize these records, the architect creates a consistent 'Golden Record'. This prevents data fragmentation where different systems maintain conflicting versions of the same customer, which is the core challenge MDM aims to solve.

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Using Duplicate Rules to merge records.

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Creating custom formula fields.

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Enabling Person Accounts for all users.

Question 3

Which design approach is best for handling a 'Data Warehouse' requirement within Salesforce when dealing with millions of records?

Accepted Answer

Implement Big Objects for high-volume storage.. For high-volume data, architects should utilize Big Objects or External Objects to prevent hitting platform storage limits and performance degradation. Big Objects are specifically designed to store and query massive amounts of data efficiently. This strategy is vital for data management, as it keeps the core CRM performance high while still maintaining accessibility to historical archives needed for operational reporting or compliance purposes.

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Use standard objects and archive periodically.

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Store all data in a single custom object.

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Use a custom field to store external reference IDs.

Question 4

A company is experiencing slow performance on reports that join multiple objects with millions of records. What architectural change should the Data Architect propose first to improve report performance?

Accepted Answer

Request custom indexes for the fields used in report filters.. Indexing is the most fundamental way to improve database performance for filtering and joining large datasets. In Salesforce, ensuring that fields used in report filters and custom indexes are properly configured is the first step. If standard indexing is insufficient, the architect may consider custom indexes or denormalizing the data model. This approach is far less intrusive and more cost-effective than re-architecting the entire data model or moving data to an external warehouse.

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Switch to an external reporting tool like Tableau or Power BI.

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Force all users to use the Reporting Snapshot feature instead of live reports.

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Convert all report types to be 'Joined Reports' to reduce data loads.

Question 5

Refer to the exhibit. An architect needs to track changes to the 'Status__c' field for auditing purposes. Based on the configuration provided, what is the best way to report on the duration a record spent in each status?

Accepted Answer

Create a custom object to store status change logs with entry and exit timestamps.. Standard Field History Tracking records changes but does not store the duration between states effectively for analytics. Creating a secondary 'Audit' object that captures snapshots of status changes via a record-triggered flow is the best approach. This allows the architect to calculate intervals between timestamps using reporting tools or custom formulas, providing the necessary visibility into business process bottlenecks that native history tables cannot easily compute.

Answer

Use the built-in Field History report type.

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Enable 'Custom History Tracking' in the Setup menu.

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Use a formula field to calculate the duration.

Question 6

A global organization uses Salesforce as its CRM and SAP as its ERP. They need to ensure that customer data remains consistent across both systems. Which approach best supports a Master Data Management strategy for this landscape?

Accepted Answer

Implement a centralized MDM hub to mediate data exchanges and manage the golden record.. Establishing a centralized hub or golden record strategy is critical for large enterprises. By defining a source of truth for specific data attributes and orchestrating updates through an integration layer, organizations prevent data drift. This approach ensures that downstream systems consume validated, reliable data, which is essential for maintaining compliance, improving reporting accuracy, and providing a unified customer experience across disparate business units and global regions.

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Perform nightly batch updates from Salesforce to SAP using flat files.

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Allow bi-directional synchronization between Salesforce and SAP without a mediation layer.

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Migrate all customer data from SAP into Salesforce to eliminate the need for an external hub.

Question 7

An enterprise architect is evaluating data storage strategies for a telecommunications client experiencing rapid data growth of fifty million call detail records annually. Which TWO strategies should the architect implement to maintain database query performance and platform data limits? Choose 2 answers.

Accepted Answer

Utilize Salesforce Big Objects to store historical call records asynchronously and index primary lookup keys.. When dealing with massive data volumes exceeding standard transactional limits, traditional custom objects will quickly degrade platform performance and storage capacity. Offloading historical analytical data to Salesforce Big Objects or external systems via Salesforce Connect preserves core transactional limits. Additionally, implementing strict data archiving strategies prevents data skew and maintains query optimization.

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Store the call detail records in standard custom objects with indexes applied to all foreign keys.

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Create hierarchical custom object structures linking every call record directly to the Account master object.

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Rely on standard Salesforce report archives to automatically move historical records out of reporting tables.

Question 8

Which of the following is a classic symptom of poor Master Data Management in a Salesforce ecosystem?

Accepted Answer

Sales and Marketing teams having conflicting views of the same customer.. Fragmented data silos often result in the same customer existing as multiple records across different systems (e.g., Salesforce, SAP, and Marketing Cloud). This leads to poor customer service, conflicting marketing messages, and inaccurate financial reporting. Identifying these symptoms is critical for a Data Architect to justify the investment in an MDM solution, as these issues directly impact the bottom line through operational inefficiency and missed sales opportunities.

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Salesforce users reporting that their dashboards load slowly.

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Excessive use of Apex triggers that cause governor limit exceptions.

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Users forgetting their passwords frequently, requiring support tickets.

Question 9

A company requires a data model to track 'Training Sessions' (which have many attendees) and 'Employees' (who attend many sessions). Which relationship is required to model this correctly?

Accepted Answer

A junction object with two Master-Detail relationships.. A many-to-many relationship is required to associate multiple Employees with multiple Training Sessions. This is implemented via a junction object. This pattern is fundamental in relational database design, as it resolves circular dependencies and allows for granular tracking of attendance, grades, or completion status per individual pairing, which cannot be achieved using simple lookups or single-parent structures on the employee or session records.

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A Lookup relationship from Employee to Training Session.

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A Master-Detail relationship on the Employee object.

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A multi-select picklist on the Employee object.

Question 10

You are auditing a Salesforce environment and discover a custom object with 20 million records. Users report that searching for records by a custom field 'External_ID__c' is extremely slow. What is the most appropriate action to take?

Accepted Answer

Mark the field as an External ID to trigger an automatic index.. When an object has large data volumes, non-indexed fields cause full table scans, which are highly inefficient and slow. By marking the 'External_ID__c' field as an External ID or unique field, Salesforce automatically creates a database index. This allows the query engine to perform a targeted lookup rather than scanning all 20 million records, providing an immediate and significant performance improvement for queries and searches involving that specific field.

Answer

Create a custom report type to better filter the data.

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Increase the batch size of the user's list view.

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Convert the custom object into a Big Object.

Question 11

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 12

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.

Answer

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 13

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 14

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.

Answer

Use a formula field

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Use a text area field

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Use a standard lookup

Question 15

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.

Answer

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 16

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.

Answer

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 17

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.

Answer

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 18

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.

Answer

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 19

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.

Answer

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 20

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.

Answer

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.