Total 257 Questions
Last Updated On : 2-Jun-2025
Preparing with Data-Architect practice test is essential to ensure success on the exam. This Salesforce SP25 test allows you to familiarize yourself with the Data-Architect exam questions format and identify your strengths and weaknesses. By practicing thoroughly, you can maximize your chances of passing the Salesforce certification spring 2025 release exam on your first attempt. Surveys from different platforms and user-reported pass rates suggest Data-Architect practice exam users are ~30-40% more likely to pass.
Universal Containers (UC) has implemented Sales Cloud and it has been noticed that Sales reps are not entering enough data to run insightful reports and dashboards. UC executives would like to monitor and measure data quality metrics. What solution addresses this requirement?
A. Use third-party AppExchange tools to monitor and measure data quality.
B. Generate reports to view the quality of sample data.
C. Use custom objects and fields to calculate data quality.
D. Export the data to an enterprise data warehouse and use BI tools for data quality.
Explanation:
Option A (✔️ Best Choice): Salesforce AppExchange offers dedicated data quality management tools (e.g., Data.com Clean, Cloudingo, DemandTools) that provide automated monitoring, scoring, and remediation for data quality issues. These tools can track missing fields, duplicates, and compliance, aligning with UC’s need to enforce data entry standards.
Option B (❌ Partial Solution): While reports can show missing data, they require manual effort and lack proactive monitoring/automation.
Option C (❌ Overly Complex): Custom objects/fields could theoretically track quality, but this is a cumbersome, maintenance-heavy approach compared to purpose-built tools.
Option D (❌ Not Real-Time): Exporting data to a warehouse introduces latency and doesn’t solve the root issue (poor data entry at the source).
Universal Containers (UC) owns several Salesforce orgs across a variety of business units. UC management has declared that it needs the ability to report on Accounts and Opportunities from each org in one place. Once the data is brought together into a global view, management would like to use advanced Al-driven analytics on the dataset. Which tool should a data architect recommend to accomplish this reporting requirement?
A.
Run standard reports and dashboards.
B.
Install a third-party AppExchange tool for multi-org reporting.
C.
Use Einstein Analytics for multi-org.
D.
Write a Python script to aggregate and visualize the data.
Use Einstein Analytics for multi-org.
Explanation:
Option C (✔️ Best Choice) – Einstein Analytics (now Tableau CRM) is Salesforce’s native AI-powered analytics platform, designed to:
Aggregate data from multiple orgs (via connectors, ETL, or Salesforce Data Federation).
Provide a unified global view of Accounts, Opportunities, etc.
Leverage AI-driven insights (predictive analytics, anomaly detection, etc.).
Option A (❌ Limited) – Standard reports/dashboards cannot pull data from multiple orgs into a single view.
Option B (❌ Alternative, but not best) – While some AppExchange tools (e.g., Gizmo, CRM Analytics connectors) can help, they lack native AI integration and may require extra setup.
Option D (❌ Not scalable) – Custom Python scripts are manual, brittle, and unsupported for enterprise reporting needs.
To avoid creating duplicate Contacts, a customer frequently uses Data Loader to upsert Contact records into Salesforce. What common error should the data architect be aware of when using upsert?
A.
Errors with duplicate external Id values within the same CSV file.
B.
Errors with records being updated and inserted in the same CSV file.
C.
Errors when a duplicate Contact name is found cause upsert to fail.
D.
Errors with using the wrong external Id will cause the load to fail.
Errors with duplicate external Id values within the same CSV file.
Explanation:
Option A (✔️ Critical Issue) – Upsert relies on unique external IDs to match records. If the same external ID appears multiple times in the CSV, Salesforce cannot determine which record to update, causing "Duplicate External ID" errors.
Option B (❌ Not an issue) – Upsert is designed to handle both inserts and updates in the same operation.
Option C (❌ Misleading) – Upsert does not check for duplicate names, only the specified external ID.
Option D (❌ Partial, but not the core issue) – While using the wrong external ID field can cause failures, it’s not the most common error compared to duplicate external IDs in the file.
Universal Containers (UC) manages Vehicle and Service History in Salesforce. Vehicle (Vehicle__ c) and Service History (Service-History__ c) are both custom objects related through a lookup relationship. Every week a batch synchronization process updates the Vehicle and Service History records in Salesforce. UC has two hours of migration window every week and is facing locking issues as part of the data migration process. What should a data architect recommend to avoid locking issues without affecting performance of data migration?
A.
Use Bulk API parallel mode for data migration
B.
Use Bulk API serial mode for data migration
C.
Insert the order in another custom object and use Batch Apex to move the records to Service_ Order__ c object.
D.
Change the lookup configuration to "Clear the value of this field" when lookup record is deleted.
Use Bulk API parallel mode for data migration
Explanation:
Option A (✔️ Best Solution) – Bulk API in parallel mode processes batches concurrently, reducing lock contention and improving performance. This is ideal for large data migrations within tight windows.
Why? Parallel mode splits the workload across multiple threads, minimizing row/table locks.
Option B (❌ Slower & Riskier) – Serial mode processes records sequentially, increasing the chance of locks and exceeding the 2-hour window.
Option C (❌ Overcomplicated) – While Batch Apex can help with complex logic, it doesn’t inherently resolve locking issues and adds unnecessary steps.
Option D (❌ Irrelevant) – This setting affects record deletion behavior, not locking during bulk updates.
Universal Containers would like to remove data silos and connect their legacy CRM together with their ERP and with Salesforce. Most of their sales team has already migrated to Salesforce for daily use, although a few users are still on the old CRM until some functionality they require is completed. Which two techniques should be used for smooth interoperability now and in the future.
A.
Replicate ongoing changes in the legacy CRM to Salesforce to facilitate a smooth transition when the legacy CRM is eventually retired.
B.
Specify the legacy CRM as the system of record during transition until it is removed from operation and fully replaced by Salesforce.
C.
Work with stakeholders to establish a Master Data Management plan for the system of record for specific objects, records, and fields.
D.
Do not connect Salesforce and the legacy CRM to each other during this transition period, but do allow both to interact with the ERP.
Replicate ongoing changes in the legacy CRM to Salesforce to facilitate a smooth transition when the legacy CRM is eventually retired.
Work with stakeholders to establish a Master Data Management plan for the system of record for specific objects, records, and fields.
Explanation:
Option A (✔️ Ensures Data Continuity)
Bidirectional sync (or replication) between the legacy CRM and Salesforce keeps data consistent for users in both systems.
Example: Use MuleSoft, Informatica, or Salesforce Connect to sync changes in real time or batches.
Option C (✔️ Critical for Long-Term Success)
Master Data Management (MDM) ensures clarity on which system owns which data (e.g., "Accounts" in Salesforce vs. "Orders" in ERP).
Prevents conflicts and duplicates by defining systems of record for each object/field during and after the transition.
Why Not the Others?
Option B (❌ Risky) – Declaring the legacy CRM as the sole system of record delays Salesforce adoption and creates dependency.
Option D (❌ Creates Silos) – Isolating Salesforce and the legacy CRM defeats the goal of removing silos and harms user experience.
A company has 12 million records, and a nightly integration queries these records. Which two areas should a Data Architect investigate during troubleshooting if queries are timing out? (Choose two.)
A.
Make sure the query doesn't contain NULL in any filter criteria.
B.
Create a formula field instead of having multiple filter criteria.
C.
Create custom indexes on the fields used in the filter criteria.
D.
Modify the integration users' profile to have View All Data.
Make sure the query doesn't contain NULL in any filter criteria.
Create custom indexes on the fields used in the filter criteria.
Explanation:
✅ A. NULL in filter criteria
Queries using WHERE field = NULL or WHERE field != NULL are problematic because they bypass indexes and require full table scans, especially on large datasets like 12 million records.
Such filters are not selective, which contributes to query timeouts.
✅ C. Custom indexes
Indexes improve query performance by allowing Salesforce to efficiently retrieve relevant records.
If fields used in WHERE clauses are not selectively indexed, the query can exceed governor limits or time out.
Data Architects should evaluate selectivity and whether custom indexes (skinny tables or external indexes) are needed.
Why Not the Others?
❌ B. Create a formula field instead of multiple filter criteria
Formula fields are not indexed by default, and using them in WHERE clauses can actually hurt performance.
Multiple filter criteria aren't inherently problematic—how selective the filters are matters more.
❌ D. Modify the integration users' profile to have View All Data
This has no impact on query performance.
It changes access rights, not how efficiently the query runs.
Universal Containers (UC) is concerned about the accuracy of their Customer information in Salesforce. They have recently created an enterprise-wide trusted source MDM for Customer data which they have certified to be accurate. UC has over 20 million unique customer records in the trusted source and Salesforce. What should an Architect recommend to ensure the data in Salesforce is identical to the MDM?
A.
Extract the Salesforce data into Excel and manually compare this against the trusted source.
B.
Load the Trusted Source data into Salesforce and run an Apex Batch job to find difference.
C.
Use an AppExchange package for Data Quality to match Salesforce data against the Trusted source.
D.
Leave the data in Salesforce alone and assume that it will auto-correct itself over time.
Use an AppExchange package for Data Quality to match Salesforce data against the Trusted source.
Explanation:
Option C (✔️ Best Practice) – AppExchange data quality tools (e.g., Informatica Cloud, Talend, Cloudingo, or DemandTools) are designed to:
Compare large datasets (20M+ records) efficiently.
Identify discrepancies between Salesforce and the MDM.
Automate cleansing/syncing to align Salesforce with the trusted source.
Support ongoing monitoring to prevent future drift.
Why Not the Others?
Option A (❌ Not Scalable) – Manual Excel comparison is error-prone and impossible at this scale (20M records).
Option B (❌ Resource-Intensive) – Apex batch jobs can work but require custom development and lack built-in matching logic (e.g., fuzzy matching).
Option D (❌ Risky) – Assuming auto-correction ignores data governance and risks reporting inaccuracies.
Universal Containers (UC) uses the following Salesforce products:
A.
Load the CSV files in Einstein Analytics and sync with Marketing Cloud prior to sending marketing communications ;
B.
Load the CSV files in an external database and sync with Marketing Cloud prior to sending marketing communications.
C.
Load the contacts directly to Marketing Cloud and have a reconciliation process to track prospects that are converted to customers.
D.
Continue to use the existing process to use Lead object to sync with Marketing Cloud and delete Lead records from Sales after the sync is complete.
Load the contacts directly to Marketing Cloud and have a reconciliation process to track prospects that are converted to customers.
Explanation:
Option C (✔️ Best Solution) – Bypassing Sales Cloud storage entirely by loading prospects directly into Marketing Cloud (via Import or Contact Builder) avoids consuming Salesforce Lead storage.
Pros:
1. No storage impact on Sales Cloud.
2. Faster marketing execution (no sync delays).
3. Reconciliation: Use Marketing Cloud’s tracking tools (e.g., Journey Builder, Data Extensions) to identify converted prospects and sync only qualified leads back to Sales Cloud.
Why Not the Others?
Option A (❌ Inefficient) – Einstein Analytics is not a storage solution and adds unnecessary complexity for prospect lists.
Option B (❌ Overhead) – External databases require additional integration costs and maintenance.
Option D (❌ Temporary Fix) – Deleting Leads post-sync risks losing data and complicates compliance/reporting.
NTO has a loyalty program to reward repeat customers. The following conditions exists:
1.Reward levels are earned based on the amount spent during the previous 12 months.
2.The program will track every item a customer has bought and grant them points for discount.
3.The program generates 100 million records each month.
NTO customer support would like to see a summary of a customer’s recent transaction and reward level(s) they have attained. Which solution should the data architect use to provide the information within the salesforce for the customer support agents?
A.
Create a custom object in salesforce to capture and store all reward program. Populate nightly from the point-of-scale system, and present on the customer record.
B.
Capture the reward program data in an external data store and present the 12 months trailing summary in salesforce using salesforce connect and then external object.
C.
Provide a button so that the agent can quickly open the point of sales system displaying the customer history.
D.
Create a custom big object to capture the reward program data and display it on the contact record and update nightly from the point-of-scale system.
Create a custom big object to capture the reward program data and display it on the contact record and update nightly from the point-of-scale system.
Explanation:
Option D (✔️ Best Solution) – Big Objects are designed for high-volume, low-frequency data (e.g., 100M records/month).
Pros:
1. Scalable storage: Handles billions of records without impacting Salesforce performance.
2. Queryable: Supports SOQL for summaries (e.g., "12-month trailing spend").
3. Integrated UI: Display summaries on Contact/Account pages via Lightning components.
Why Not the Others?
Option A (❌ Storage Bloat) – Standard/custom objects hit storage limits with 100M monthly records.
Option B (❌ Latency & Complexity) – External objects via Salesforce Connect introduce real-time query delays and require external infrastructure.
Option C (❌ Poor UX) – Switching systems disrupts support workflows and lacks Salesforce integration.
Universal Containers (UC) is a major supplier of office supplies. Some products are produced by UC and some by other manufacturers. Recently, a number of customers have complained that product descriptions on the invoices do not match the descriptions in the online catalog and on some of the order confirmations (e.g., "ballpoint pen" in the catalog and "pen" on the invoice, and item color labels are inconsistent: "what vs. "White" or "blk" vs. "Black"). All product data is consolidated in the company data warehouse and pushed to Salesforce to generate quotes and invoices. The online catalog and webshop is a Salesforce Customer Community solution. What is a correct technique UC should use to solve the data inconsistency?
A.
Change integration to let product master systems update product data directly in Salesforce via the Salesforce API.
B.
Add custom fields to the Product standard object in Salesforce to store data from the different source systems.
C.
Define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse.
D.
Build Apex Triggers in Salesforce that ensure products have the correct names and labels after data is loaded into salesforce.
Define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse.
Explanation:
Option C (✔️ Best Solution) – Data Taxonomy standardizes naming conventions (e.g., "Ballpoint Pen" instead of "pen") and formats (e.g., "Black" instead of "blk") at the source (data warehouse) before pushing to Salesforce.
Pros:
1. Ensures consistent product descriptions across all systems (catalog, invoices, quotes).
2. Centralized governance: Fixes inconsistencies upstream rather than in each system.
3. Scalable: Applies to future integrations.
Why Not the Others?
Option A (❌ Fragile) – Letting multiple systems update Salesforce directly without standardization perpetuates inconsistencies.
Option B (❌ Redundant) – Custom fields store variants but don’t solve the root issue (lack of standardization).
Option D (❌ Band-Aid Fix) – Triggers add technical debt and fail if data warehouse pushes incorrect values.
| Page 1 out of 26 Pages |
| Key Topic Area | SalesforceExams.com Advantage | Common Pitfalls Without Practice Tests |
|---|---|---|
| Data Modeling & Management (20%) | Mastery of complex object relationships and normalization principles | Struggle with scalable data model design and governance planning |
| Large Data Volume (15%) | Clear understanding of indexing strategies and selective query optimization | Performance issues in solutions; missing critical considerations for data skew |
| Data Migration (15%) | Practical experience with data loader limitations and ETL tool selection | Underestimating complexity of multi-object migrations and data cleansing requirements |
| Data Governance (12%) | Strong grasp of regulatory compliance frameworks and audit trails | Incomplete security models and insufficient data retention strategies |
| Salesforce Reporting (14%) | Expertise in cross-object reporting and dashboard best practices | Creating report types that dont scale or miss critical business insights |
| Master Data Management (12%) | Confident implementation of duplicate management and matching rules | Poor duplicate prevention strategies and global picklist management |
| Architecture Design (12%) | Balanced approach to tradeoffs between conflicting requirements | Over-engineering solutions or missing critical integration limitations |
Copyright © - All Rights Reserved