Salesforce Certified Data Architect Exam Dumps & Practice Test Questions
Question 1
Northern Trail Outfitters (NTO) recently experienced confusion among users due to the sudden appearance of unfamiliar fields on their Salesforce pages. Teams are unsure about the purpose and necessity of these fields. Leadership is concerned about the lack of governance over field changes.
As a Data Architect, which strategy would you suggest to improve data consistency and change communication?
A. Generate reports to identify frequently blank fields and populate them using external data sources.
B. Add descriptions and mark all necessary fields as required for data entry.
C. Establish a centralized data dictionary and implement a formalized governance process for modifying shared objects.
D. Use validation rules with detailed error messages to inform users how to complete fields correctly.
Answer: C
Explanation:
In the given scenario, Northern Trail Outfitters (NTO) is dealing with a lack of clarity and confusion among users due to the appearance of unfamiliar fields in their Salesforce environment. This issue is compounded by the absence of proper communication and governance, which is now becoming a leadership concern. The best solution for this type of problem is not simply technical but organizational and strategic—ensuring structured control over changes and clear documentation of what data fields mean and how they are used.
Option C is the most comprehensive and sustainable approach. Establishing a centralized data dictionary ensures that every field has a clearly defined purpose, owner, and usage guidelines. A formalized governance process provides oversight and control over the creation, modification, and deletion of data elements, particularly in shared objects across business units. This promotes transparency and alignment between teams and ensures that all stakeholders are informed before any changes are implemented. This also provides a platform to assess business impact and avoids accidental disruptions to business processes.
Option A, generating reports and populating blank fields from external sources, does not address the underlying confusion or governance issue. While identifying underused fields can be useful, automatically populating them introduces additional risk without solving the problem of users not understanding why those fields exist.
Option B, marking fields as required and adding descriptions, helps on a micro-level, but lacks the strategic oversight needed to control how and when fields are introduced. Making a field required without broader impact analysis could disrupt data entry across multiple teams or processes.
Option D, using validation rules to provide field-level guidance, is good practice for ensuring correct data entry, but again, it does not solve the root problem of field governance. Validation rules should complement, not replace, a structured change control and documentation process.
Ultimately, successful data governance requires more than just technical enforcement—it needs a well-maintained data catalog, communication with stakeholders, and a defined change management process, all of which are embodied in Option C. This ensures long-term clarity, alignment, and trust in the organization’s data landscape.
Question 2
Universal Containers uploads a large volume of lead records every week and temporarily disables validation rules to ensure smoother processing. This has raised concerns about inconsistent data quality.
As a Data Architect, what should you recommend to uphold lead data quality without changing the current import process?
A. Keep validation rules enabled during the data import.
B. Build a Batch Apex class to clean and verify the data post-import.
C. Re-enable validation rules immediately after each data load.
D. Apply data validation and cleansing steps before importing leads into Salesforce.
Answer: D
Explanation:
The challenge Universal Containers is facing is the conflict between performance and data quality. Disabling validation rules may speed up data imports but compromises the integrity and consistency of the information in Salesforce. However, the question explicitly states that the current import process should not be changed, so options that disrupt the current flow (like enabling validation during import) are not viable.
Option D is the most strategic and realistic solution. Applying data validation and cleansing before importing ensures that the data entering Salesforce already conforms to required business rules and standards. This can be done through ETL tools, pre-load scripts, or external data quality platforms. This approach respects the existing import process while addressing the core issue of inconsistent or poor-quality data.
Option A, keeping validation rules enabled during import, would likely result in failed imports or performance degradation. Since the existing process already circumvents this, reverting back would disrupt the workflow and may not be acceptable to the business.
Option B, using Batch Apex to clean and verify the data after it has been imported, is technically feasible but reactive rather than proactive. This method may also lead to increased complexity, potential delays in error correction, and conflict with downstream processes that rely on timely and accurate data.
Option C, re-enabling validation rules after the import, is standard practice, but it doesn’t address the data already imported incorrectly. This doesn’t solve the actual problem of maintaining high-quality data at the point of entry.
Therefore, the most effective and non-disruptive approach is D. By shifting the data quality control to the pre-import phase, Universal Containers can preserve the performance of its bulk import process while ensuring that incoming data complies with business standards and expectations. This solution aligns with data governance best practices and contributes to a reliable and scalable data architecture.
Question 3
A client wants users to access over 150 million Sales Order records stored in an on-premise ERP system. The data is read-only and must be viewable from within Salesforce.
What is the most efficient solution to provide this access?
A. Create custom Salesforce objects to hold the Sales Order records.
B. Use External Objects to connect Salesforce with the ERP system.
C. Store the Sales Orders using Salesforce Big Objects.
D. Utilize the standard Salesforce Order object to represent the data.
Answer: B
Explanation:
In this scenario, the client needs to expose a very large volume of read-only data (over 150 million records) from an on-premise ERP system in Salesforce. Importantly, the data is not transactional within Salesforce—users need to view, not create or modify, the records.
Option B, using External Objects, is the most efficient and scalable solution for this requirement. External Objects are part of Salesforce’s Salesforce Connect feature, which allows real-time integration with external systems using standards like OData, custom adapters, or middleware. External Objects do not store the data inside Salesforce, but instead act as a proxy that maps external data sources to Salesforce in a way that makes them appear like Salesforce objects to end users. This allows seamless UI integration with minimal storage usage in Salesforce, making it ideal for very large datasets that are read-only and reside externally.
Option A, creating custom objects in Salesforce to store 150 million records, would be highly inefficient. Salesforce has storage limits per org, and importing such a large dataset would be expensive and potentially impact performance. Also, the need to regularly synchronize this data with the ERP system adds unnecessary complexity.
Option C, using Big Objects, is a scalable Salesforce-native solution for storing massive volumes of data, but Big Objects are designed primarily for archival and analytical purposes, not for interactive or relational UI display. Big Objects do not support standard UI features or relationships in the same way External Objects do. Additionally, Big Objects are not suitable for external real-time data, as they are stored within Salesforce and must be loaded through ETL or custom processes.
Option D, using the standard Order object, assumes that the ERP system data would be migrated into Salesforce, which again leads to scalability and storage concerns. The standard Order object is meant for managing order lifecycle within Salesforce, not for representing external ERP data in bulk.
Therefore, Option B provides the optimal balance of scalability, performance, real-time access, and cost-efficiency, allowing users to view external ERP Sales Order data without replicating it into Salesforce. It directly supports the business requirements while avoiding limitations related to storage, sync, and UI performance.
Question 4
Universal Containers requires that any user with access to an Opportunity should also have read-only access to the related Account, even if the Account hasn’t been directly shared.
What is the best method to implement this access control?
A. Rely on Salesforce's built-in implicit sharing functionality.
B. Create owner-based sharing rules to grant access to the Account.
C. Manually assign Account Team members to provide access.
D. Define a criteria-based sharing rule based on Opportunity access.
Answer: A
Explanation:
Salesforce provides a hierarchical data access model that includes implicit sharing, which automatically grants certain access rights based on record relationships. One such rule is that if a user has access to a child record, such as an Opportunity, Salesforce implicitly grants them read-only access to the parent Account—even if that Account hasn’t been directly shared.
Option A is correct because implicit sharing is the default behavior in Salesforce. For example, if a user can view or edit an Opportunity, they automatically get read-only access to its parent Account, regardless of the Account’s sharing rules. This ensures relational context and supports business use cases like viewing the Account name, contact info, and other associated records when working on Opportunities.
Option B, using owner-based sharing rules, does not address the requirement effectively. Sharing rules based on record ownership are not granular to child-parent relationships and could inadvertently grant access to more records than necessary.
Option C, manually assigning Account Team members, is not scalable. It requires administrative or user intervention to assign people to each Account, which is impractical in environments with large volumes of data or frequent record changes.
Option D, defining criteria-based sharing rules, is not viable because sharing rules cannot reference Opportunity access when evaluating Account sharing criteria. Sharing rules operate on object-level attributes and cannot be used to grant access based on related object visibility.
Since the business requirement aligns with behavior that is already natively handled by the Salesforce platform through implicit sharing, the best and most efficient approach is Option A. It meets the need without additional configuration, ensuring consistent access control in a scalable and supportable manner.
Question 5
Universal Containers manages over 10 million inventory records in a cloud-hosted database. They want to allow users in Sales Cloud to view this inventory data without importing it.
What three considerations should guide the decision to use Salesforce Connect for this integration? (Choose three)
A. The requirement for up-to-date, real-time data access.
B. The need to only retrieve small subsets of external data.
C. The necessity for secure access over a private network.
D. A desire to store select portions of data in Salesforce.
E. The preference to avoid duplicating or storing large datasets inside Salesforce.
Answer: A, B, E
Explanation:
Salesforce Connect is designed to enable Salesforce to interact with external data sources in real time by mapping their data into External Objects. This architecture avoids the need to import or duplicate large datasets into the Salesforce platform, making it a scalable and efficient solution for companies with massive volumes of external data, like Universal Containers.
Option A is correct. One of the key benefits of Salesforce Connect is real-time data access. Since the data remains in the external system, any changes made in the cloud-hosted database are immediately visible in Salesforce when users view the records. This real-time access is ideal when data must always reflect the current state in the source system without synchronization lag.
Option B is also correct. Salesforce Connect is most efficient when used to access small, filtered subsets of large data sets. For instance, queries should be optimized to retrieve only relevant records (e.g., via filters or views) rather than pulling large volumes at once. This is due to performance and API limits, which can become bottlenecks if the full external dataset is queried regularly.
Option E is correct as well. The fundamental purpose of Salesforce Connect is to avoid duplicating or storing large external datasets in Salesforce. Importing 10+ million inventory records into Salesforce would be cost-prohibitive and performance-impacting due to platform storage limitations and governor limits. By using Salesforce Connect, Universal Containers can leverage existing infrastructure while minimizing Salesforce data storage usage.
Option C is not necessarily a consideration specific to Salesforce Connect. While secure access is important for any integration, Salesforce Connect primarily operates over public protocols like OData 2.0/4.0 or REST endpoints. For private network access, middleware (e.g., MuleSoft) or VPN tunnels may be required. However, that is a network design issue, not a determining factor for choosing Salesforce Connect itself.
Option D suggests the desire to store select portions of external data inside Salesforce, which contradicts the main value of Salesforce Connect. If the business goal were to selectively store data, a hybrid approach with scheduled ETL loads would be more appropriate.
Therefore, the most relevant considerations for using Salesforce Connect in this scenario are A, B, and E.
Question 6
Universal Containers wants to retain only the most recent two years of data within Salesforce and archive older data externally to address storage limitations.
Which strategy should the Data Architect recommend?
A. Move all records to an external system and delete them from Salesforce.
B. Identify and transfer records older than two years to external storage, then remove them from Salesforce.
C. Offload all data to an external platform and delete selected historical records.
D. Use a third-party backup tool to store all data externally.
Answer: B
Explanation:
The scenario describes a common data retention and archival requirement where only recent data (last two years) needs to be available within Salesforce, while older records should be archived externally to manage storage usage and cost. The solution should strike a balance between operational efficiency, data governance, and performance optimization.
Option B is the most suitable recommendation. This approach involves identifying records that are older than two years, transferring them to an external storage solution, and then deleting them from Salesforce. This aligns with the requirement to retain only recent data in Salesforce while keeping older data accessible outside the platform if needed. This strategy is efficient, cost-effective, and commonly implemented using archival solutions, middleware (e.g., MuleSoft, Informatica), or custom APIs. It ensures that Salesforce storage remains optimized and reduces risk of hitting data limits.
Option A, moving all records out of Salesforce, is an overly aggressive approach that eliminates live access to any data in Salesforce, which contradicts the business requirement of keeping the most recent two years within the system. It would severely degrade user experience and could complicate reporting or ongoing processes.
Option C, offloading all data and only deleting selected historical records, is inconsistent. If you're offloading everything, the concept of selective deletion within Salesforce becomes irrelevant. Also, this introduces unnecessary complexity, as maintaining all data externally could lead to performance challenges in external systems and complicate integration.
Option D, using a third-party backup tool, focuses more on disaster recovery than operational archiving. Backup tools (like OwnBackup, Spanning) are primarily designed to help recover from data loss or corruption, not to manage real-time access to archived records. This approach does not fulfill the business need to manage storage proactively by removing older data from Salesforce and may not support data querying or reporting in the same way an archival system would.
Therefore, Option B offers the right balance of performance, cost-efficiency, and governance by selectively archiving older records while retaining recent data for operational use in Salesforce.
Question 7
Universal Containers is preparing to load 100,000 records across multiple objects into a highly automated Salesforce org over a weekend. Many triggers, workflows, and validations are not designed for bulk imports.
What is the best approach to minimize import errors and prevent process conflicts?
A. Temporarily disable validation rules, triggers, and automation before the data load.
B. Set up and enforce duplicate detection and matching rules beforehand.
C. Refactor and bulkify triggers to handle large data volumes.
D. Split the import into smaller batches and stagger the processing over time.
Answer: A
Explanation:
When planning a large-scale data load—in this case, 100,000 records across multiple objects—into a Salesforce org that contains a high level of automation (such as workflows, validation rules, process builders, and Apex triggers), it is essential to mitigate the risk of import failures, errors, and system strain. This is particularly true when existing automation is not built for bulk processing, which can lead to governor limit exceptions, incorrect data handling, or timeouts.
Option A—temporarily disabling validation rules, triggers, and automation—is the most practical and effective approach in such a scenario. This strategy allows the data to be inserted cleanly and efficiently, without interference from potentially misconfigured or non-bulkified business logic. It ensures that automation doesn't misfire or process incomplete data during import, and avoids cascading errors or excessive consumption of platform resources.
Once the data load is complete, automation can be re-enabled, and post-load data quality processes such as re-validating data or triggering appropriate flows can be initiated manually or via scheduled automation. Importantly, disabling these elements must be controlled and well-documented, with proper testing after reactivation to ensure that business rules and processes resume normal operation.
Option B, setting up duplicate detection and matching rules, is useful for maintaining data quality but does not directly prevent automation conflicts or import failures caused by triggers and validations. It's part of good data hygiene, but not the central solution to the specific issue in this scenario.
Option C, refactoring and bulkifying all existing triggers, is technically ideal for long-term scalability and best practice compliance. However, it is not feasible as a short-term solution for an import scheduled over a weekend. Refactoring code is resource-intensive and requires testing, deployment planning, and change management, which is not practical on short notice.
Option D, breaking the data load into smaller batches and staggering them, may help manage system limits, but does not solve the root issue of poorly optimized or non-bulkified automation. In fact, processing small batches could exacerbate the problem if the triggers and workflows execute inefficiently on each batch.
Thus, the best immediate solution for this planned import is Option A, as it minimizes risk and provides control over how and when automation is re-engaged post-import.
Question 8
Sales reps at Universal Containers need visibility into the status of customer orders, which are managed in an external ERP system. The ERP system is not accessible to the reps.
What is the most effective solution to expose this order data within Salesforce?
A. Use Salesforce Canvas to embed the ERP interface in a tab.
B. Build a real-time integration that pulls order details into Salesforce on demand.
C. Schedule batch processes to push ERP data into Salesforce regularly.
D. Implement Salesforce Connect to display ERP order data as external objects.
Answer: D
Explanation:
The business requirement here is to provide Salesforce users (sales reps) with visibility into order status that is managed in an external ERP system, without giving them direct access to the ERP itself. The ideal solution must provide seamless access, minimize data duplication, and ensure real-time or near-real-time visibility into the ERP data.
Option D, using Salesforce Connect to display ERP order data as external objects, is the most effective and scalable solution. Salesforce Connect allows Salesforce to access data stored outside of its platform (in systems like ERP databases) through External Objects, without importing or storing the data internally. These objects appear like native Salesforce records and can be integrated into the UI (e.g., related lists, page layouts, reports) while querying the ERP data in real time or near-real time depending on the adapter used (e.g., OData, custom Apex adapter, middleware).
This solution avoids the overhead of synchronizing massive amounts of data, keeps Salesforce storage costs down, and maintains data fidelity by ensuring users always see the most recent information directly from the source.
Option A, embedding the ERP interface using Salesforce Canvas, could technically provide access to the ERP UI, but it assumes that sales reps can authenticate into the ERP system, which the prompt explicitly says is not allowed. Additionally, Canvas may not offer the same level of integration with Salesforce UI features like reporting, search, or workflows.
Option B, creating a real-time integration to pull data into Salesforce on demand, may solve the visibility issue but introduces complexity and potential performance issues, especially if many users request order data simultaneously. Also, it still involves bringing data into Salesforce, which the question does not require.
Option C, pushing ERP data into Salesforce via scheduled batch processes, would result in delayed visibility, depending on the schedule. This approach also duplicates data into Salesforce, consuming storage and requiring sync logic and monitoring.
Therefore, the most efficient, low-overhead, and user-friendly option that meets the stated constraints is Option D. Salesforce Connect is designed exactly for this type of external data access scenario, particularly when the source is read-only or controlled externally.
Question 9
A large global B2C client wants distributors to enter Sales Orders in Salesforce, with pricing based on regional differences. Sales Orders will be stored in the Opportunity object and marked closed once fulfilled.
How should the data model be designed to manage pricing and distributor access efficiently?
A. Link Opportunities to a custom object containing regional prices, and share it with distributors.
B. Require distributors to manually input the appropriate prices on Opportunities.
C. Set up region-specific Price Books and assign them to the respective distributors.
D. Use custom fields and Apex triggers to automatically apply regional pricing.
Answer: C
Explanation:
Managing region-based pricing effectively in Salesforce requires a solution that is scalable, configurable, and compatible with standard objects and functionality. In this scenario, the client uses the Opportunity object to store Sales Orders, which implies use of standard sales processes. The goal is to support regional pricing and allow distributors to enter Sales Orders, while also ensuring efficient data access control.
Option C, using region-specific Price Books, is the most appropriate and scalable solution. Salesforce provides Standard Price Books and Custom Price Books, allowing you to define product pricing based on business criteria such as geography, customer type, or distribution channel. Each distributor can be assigned the relevant Price Book, which ensures that when they create Opportunities (Sales Orders), they can only select products with pricing specific to their region.
Price Books are also supported natively in the Opportunity and OpportunityLineItem model, making them ideal for scenarios involving product selection and pricing. Moreover, sharing Price Books and restricting access based on roles or territories ensures clean data governance and distributor-specific visibility.
Option A, linking Opportunities to a custom object with regional prices, could work, but introduces unnecessary complexity. You would need to build custom relationships, implement lookup logic, and enforce price application manually or through code. This approach bypasses standard Salesforce features and increases maintenance overhead.
Option B, requiring distributors to manually enter prices, is prone to human error, inconsistent data, and compliance issues. It undermines control over pricing policy and leads to poor data quality, especially in a high-volume global B2C environment.
Option D, using custom fields and Apex triggers to enforce pricing rules, is technically possible but introduces custom code maintenance, governor limit risks, and complex testing. It should only be considered if standard features (like Price Books) do not meet specific business requirements—which is not the case here.
In summary, Option C is the best practice and Salesforce-recommended approach for implementing regionally segmented pricing in a scalable, maintainable way while ensuring that distributors can access and apply pricing appropriate to their region.
Question 10
Universal Containers receives CSV files of prospect data from third-party vendors and loads them into the Lead object in Salesforce, which is then synced with Marketing Cloud. he increasing volume of Leads is causing storage concerns.
What is the best way to address the storage issue while maintaining marketing effectiveness?
A. Import the CSV files into an external database and sync the data with Marketing Cloud.
B. Continue loading Leads into Salesforce, then delete them after syncing to Marketing Cloud.
C. Import the data directly into Marketing Cloud and implement tracking for converted prospects.
D. Load the files into Einstein Analytics and then integrate with Marketing Cloud.
Answer: C
Explanation:
This scenario presents a common issue: high-volume lead ingestion from third-party vendors, followed by marketing execution through Marketing Cloud, but with Salesforce storage constraints becoming a concern. The requirement is to maintain marketing effectiveness while reducing the storage footprint within Salesforce.
Option C, importing the data directly into Marketing Cloud and implementing prospect tracking, is the most efficient and effective solution. Marketing Cloud is designed to handle large volumes of marketing data, and supports direct data ingestion from flat files, APIs, or data extensions. By loading the prospect data directly into Marketing Cloud, the organization avoids populating the Lead object in Salesforce, thus reducing data storage consumption in the core CRM platform.
Moreover, Marketing Cloud Connect allows bidirectional syncing between Salesforce and Marketing Cloud. Once a prospect engages and becomes qualified (e.g., clicks on emails, fills out forms), Marketing Cloud can pass back high-intent leads to Salesforce via automation (e.g., Journey Builder and CRM connector). This means only relevant leads are pushed into Salesforce, optimizing CRM storage while preserving the effectiveness of lead nurturing campaigns.
Option A, importing the data into an external database and syncing with Marketing Cloud, introduces unnecessary architecture and integration complexity. While it may serve a similar function, it requires additional infrastructure, security, and sync logic that can be handled more elegantly within Marketing Cloud.
Option B, continuing to load leads into Salesforce and then deleting them after syncing, is inefficient and risky. It relies on precise timing coordination and introduces potential data loss or inconsistencies, especially if leads are deleted before full processing or tracking is complete.
Option D, using Einstein Analytics (now CRM Analytics), is inappropriate for marketing execution. Einstein Analytics is a BI tool, not a data ingestion or email campaign platform. It does not serve as a staging area or sync mechanism for Marketing Cloud.
Therefore, Option C provides the best approach—offloading initial prospect data to Marketing Cloud, preserving core CRM storage, and retaining full marketing capabilities including tracking and segmentation.
