C100DEV Data Modeling Practice Question
A financial reporting system reads account documents that each contain a nested array of the last 90 daily balance snapshots. Analysts run aggregations that only need the current balance and account type, but the full snapshot array is being loaded on every read. Which schema pattern most directly reduces the working set size for these read-heavy analytics queries?
⚠ Common exam trap
Test-takers frequently confuse patterns that reduce computation cost, such as Computed, with patterns that reduce document size, such as Subset.
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
✓
Subset Pattern, keeping only the most recent snapshots in the main document and archiving older ones in a separate collection.
The Subset Pattern is specifically designed for documents with a large array where only a small portion is accessed regularly. By retaining the frequently used snapshots in the main document and relocating the rest, the document shrinks, the working set fits more easily in RAM, and read-heavy analytics touch far less data while still returning the fields analysts require.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Subset Pattern, keeping only the most recent snapshots in the main document and archiving older ones in a separate collection.
Why this is correct
The Subset Pattern stores only the portion of data most frequently accessed in the main document while moving the rest to another collection. Since analysts only need current balance and type, trimming the 90-day array dramatically reduces document size and the working set, improving read performance without changing the analytics access path.
- ✗
Schema Versioning Pattern, adding a version field so old documents can be migrated incrementally.
Why it's wrong here
Schema Versioning helps manage migrations when document shapes change over time. It does not reduce the size of the snapshot array or decrease the amount of data read during analytics queries, so it fails to address the working set concern described in the scenario.
- ✗
Computed Pattern, precomputing derived totals into new fields on the account document.
Why it's wrong here
The Computed Pattern stores precalculated values to avoid recomputation at read time. It reduces CPU work but does not shrink the document, so the large snapshot array is still read from disk and pulled into memory, leaving the working set size unchanged for the analytics workload.
- ✗
Bucket Pattern, grouping many accounts into a single document with shared metadata.
Why it's wrong here
The Bucket Pattern is designed for time-series data, grouping measurements into buckets to reduce document count. Applying it here would actually consolidate account data and complicate per-account reads, and it does not target the specific problem of a bloated snapshot array inflating each account document.
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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 MongoDB exam blueprint
This C100DEV practice question is part of Courseiva's free MongoDB 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 C100DEV exam.