SF-Data-Arch Large Data Volume Considerations Practice Question
A Salesforce org has a custom object Invoice__c with 8 million records. The business requires a dashboard that shows the total invoice amount grouped by Account and by Fiscal Year. The dashboard must refresh quickly, even during peak usage. The Invoice__c object has a lookup to Account and a formula field Fiscal_Year__c that extracts the year from Invoice_Date__c. What is the most appropriate design to support this dashboard efficiently?
⚠ Common exam trap
The trap here is assuming that a summary report or on-demand Apex aggregation can scale to millions of records without pre-aggregation, when in fact formula-field grouping forces full scans and governor limits apply.
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
✓
Create a custom object Invoice_Summary__c that stores pre-aggregated totals per Account and Fiscal Year, and update it via batch Apex or scheduled flow when invoices change.
Pre-aggregating data into a summary object is the most reliable way to keep a dashboard fast when the source object has millions of records. By storing totals per Account and Fiscal Year, the dashboard queries a small, indexed dataset instead of scanning Invoice__c. Asynchronous maintenance via batch Apex or scheduled flow avoids impacting user transactions and keeps the summary current enough for reporting.
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 Big Object indexing on Invoice__c and point the dashboard to a Big Object-backed report.
Why it's wrong here
Big Objects are not reportable in standard reports and dashboards, and they cannot be used as the primary source for dashboard components. Big Objects are designed for archival and high-volume ingest, not interactive aggregation. Indexing a Big Object does not make it available to dashboards. This option misapplies the technology and would not satisfy the dashboard requirement.
- ✗
Build a custom Lightning Web Component that calls Apex to aggregate Invoice__c records on demand and display the results on the dashboard.
Why it's wrong here
On-demand Apex aggregation over 8 million records will hit governor limits for SOQL rows and CPU time. Even with aggregate SOQL, grouping by a formula field is non-selective and will cause full table scans. The dashboard would be slow or fail, and it adds maintenance overhead. This is not a scalable LDV design.
- ✗
Create a summary report grouped by Account and Fiscal_Year__c, and add it as a dashboard component with a scheduled refresh.
Why it's wrong here
Summary reports on large objects execute aggregations at runtime across millions of rows. Grouping by a formula field prevents indexing and forces full scans, so the dashboard will be slow and may time out. Scheduling the refresh does not reduce query cost and can still fail during peak load. This approach does not meet the performance requirement for 8 million records.
- ✓
Create a custom object Invoice_Summary__c that stores pre-aggregated totals per Account and Fiscal Year, and update it via batch Apex or scheduled flow when invoices change.
Why this is correct
Pre-aggregating into a summary object reduces the dashboard query to a small number of rows, so it remains fast regardless of Invoice__c volume. Batch Apex or scheduled flow can maintain the summary asynchronously, avoiding user-facing delays. This pattern is a standard LDV optimization because it decouples heavy aggregation from interactive reporting and keeps dashboard components selective and lightweight.
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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.