SF-Data-Arch Salesforce Data Management Practice Question
Universal Containers is implementing a data governance framework. They need to ensure that data quality is maintained across multiple business units. Which two practices should the data architect recommend to enforce data quality at the point of entry? (Choose two.)
⚠ Common exam trap
The trap here is choosing reactive measures like reports or nightly corrections instead of proactive point-of-entry controls.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement duplicate rules to block or alert on duplicate records.
To enforce data quality at the point of entry, the architect should implement validation rules to reject invalid data and duplicate rules to prevent duplicates. These are proactive controls that operate when records are created or updated. They are standard Salesforce features that can be configured declaratively and apply across all entry points, including UI, API, and integrations, ensuring consistent data governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable field history tracking on all fields to audit changes.
Why it's wrong here
Field history tracking records changes to fields for auditing purposes, but it does not enforce data quality at the point of entry. It simply logs what was changed, including invalid values if they bypass other rules. It is a monitoring tool, not a preventative measure. Enabling it on all fields would also consume significant storage without improving data quality.
- ✗
Use Data Loader to perform a nightly update to correct inconsistencies.
Why it's wrong here
Using Data Loader for nightly corrections is a reactive data cleansing process. It does not stop bad data from entering the system during the day. While it can help maintain data quality over time, it is not a point-of-entry enforcement mechanism. This approach can also introduce errors if not carefully managed and does not scale well for real-time data entry.
- ✗
Schedule a weekly report to identify records with missing values.
Why it's wrong here
A weekly report identifies data quality issues after they have been entered, which is a reactive approach. It does not prevent invalid or incomplete data from being saved in the first place. For enforcing data quality at the point of entry, proactive measures like validation rules and duplicate rules are required. Reports are useful for monitoring but do not enforce quality.
- ✓
Implement duplicate rules to block or alert on duplicate records.
Why this is correct
Duplicate rules help maintain data quality by identifying potential duplicate records during creation or editing. They can be configured to block saves or allow with an alert, depending on business requirements. This prevents duplicate data from entering the system, which is essential for a single source of truth and data governance across business units.
- ✓
Define validation rules on critical fields to prevent invalid data from being saved.
Why this is correct
Validation rules enforce data integrity by checking that entered data meets specific criteria before a record is saved. They can be applied to fields across objects and are executed on all record save operations, including API and user interface. This ensures that invalid data is rejected at the point of entry, which is a core data quality practice.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Salesforce exam blueprint
This SF-Data-Arch practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SF-Data-Arch exam.