SF-Data-Arch Large Data Volume Considerations Practice Question
Which design pattern effectively handles high-volume record updates while avoiding 'Too many SOQL queries' errors?
⚠ Common exam trap
Candidates commonly write queries inside iterative loops to fetch related parent data, quickly exhausting SOQL query limits instead of utilizing collection mapping techniques.
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
✓
Use a Map to cache related records.
The use of Maps for caching parent records is essential to avoid repeated querying. By querying all necessary parent records once, storing them in a Map, and accessing them by ID in a loop, you reduce the SOQL query count to one. This pattern is the industry standard for handling large collections of records without hitting governor limits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform a SOQL query inside the trigger loop.
Why it's wrong here
Querying inside a loop will quickly exhaust the SOQL governor limit (100 queries) as the record count grows. This is a critical antipattern in Salesforce development that will cause failures on any batch operation with more than a handful of records, making it entirely unsuitable for large data volumes.
- ✓
Use a Map to cache related records.
Why this is correct
Caching related parent records in a Map ensures that SOQL queries are performed only once for the entire batch. This minimizes resource consumption and prevents governor limit violations, allowing developers to process thousands of records efficiently, which is vital for high-volume data operations in the Salesforce platform.
- ✗
Use the Future method for every record update.
Why it's wrong here
Using @future methods for every record creates a massive volume of asynchronous jobs, which can lead to hit queue limits and slow down processing. Furthermore, each job consumes its own governor limits, making this an inefficient way to handle updates for large volumes of data.
- ✗
Call the update DML statement inside the loop.
Why it's wrong here
Executing DML statements inside a loop consumes DML governor limits and significantly increases processing time. This practice is extremely inefficient and will lead to governor limit violations as the number of records increases, preventing the batch job from completing successfully in high-volume scenarios.
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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
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