C100DEV Data Modeling Practice Question
An e-commerce application models product reviews inside an array on the product document. As popular products accumulate hundreds of thousands of reviews, write operations and document fetch operations experience performance degradation. Which data modeling pattern best resolves this issue?
⚠ Common exam trap
Candidates often assume embedding is always superior in MongoDB, forgetting that arrays growing without a predictable upper bound eventually violate the 16MB document size limit and degrade write performance.
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 the Bucket Pattern by storing a limited number of reviews per document alongside grouping metadata, creating new bucket documents as needed.
Migrating from an unbounded embedded array to the Bucket Pattern divides reviews into manageable document chunks based on a count or time threshold. This prevents documents from exceeding the 16MB limit and reduces RAM pressure. Managing growth ensures predictable update performance and maintains indexing efficiency across high-throughput collections in production workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep the array embedded but add a partial index to speed up searches on the most recent reviews inside the array.
Why it's wrong here
Partial indexes improve query performance for specific subsets of documents, but they cannot prevent individual documents from exceeding the 16MB maximum size limit caused by unbounded array growth. The physical document structure remains problematic for updates.
- ✗
Move every review into its own separate collection and establish a manual join reference back to the parent product ID.
Why it's wrong here
A separate collection with a manual reference still requires the application to perform a second query and join, so fetch performance does not improve. It is tempting because unbounded array growth is the recognised anti-pattern, and would be correct if reviews were accessed independently of their product.
- ✓
Implement the Bucket Pattern by storing a limited number of reviews per document alongside grouping metadata, creating new bucket documents as needed.
Why this is correct
The Bucket Pattern caps reviews per document and spreads them across multiple bucket documents with grouping metadata. This bounds individual document size, so reads and writes no longer degrade as reviews accumulate, resolving the unbounded array growth.
- ✗
Convert the reviews array into a capped collection and use tailing cursors to push updates directly to the product document.
Why it's wrong here
Capped collections have a fixed size and overwrite oldest documents, so reviews would be silently discarded rather than retained; tailing cursors also cannot push writes into the parent product document. Capped collections suit high-throughput append-only logs, such as audit trails or event streams, where loss of old entries is acceptable.
Visual reference
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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.