C100DEV MongoDB Overview and Document Model Practice Question
Which document design pattern should be used to store a one-to-many relationship where the child documents are frequently accessed together with the parent?
⚠ Common exam trap
Candidates frequently try to normalize every relationship into separate collections, overlooking the performance benefits of embedding for one-to-many relationships.
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
✓
Embed child documents directly as an array within the parent document.
The embedding pattern is preferred for one-to-many relationships where children are always retrieved alongside the parent. This pattern minimizes the number of required read operations by ensuring all necessary data is located within a single document. This reduces latency and eliminates the need for expensive application-level joins, resulting in highly performant read operations for typical read-heavy application workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize the data by creating a separate collection for child documents.
Why it's wrong here
Normalization is generally discouraged for related data that is frequently accessed together. Creating a separate collection requires multiple roundtrips or a $lookup aggregation stage, which is significantly slower than reading an embedded document. This approach should only be reserved for cases where child data is accessed independently.
- ✓
Embed child documents directly as an array within the parent document.
Why this is correct
Embedding captures the relationship within a single atomic unit. By storing children in an array, you allow the database to return the entire hierarchical data structure in a single operation. This improves performance and simplifies application logic because the database handles the retrieval of the entire record structure.
- ✗
Store the child data in a separate database to isolate the write load.
Why it's wrong here
Spreading data across databases for a single one-to-many relationship creates unnecessary complexity for data management and integrity. It does not provide any performance benefit for read operations and makes atomic updates across the relationship nearly impossible to enforce, leading to eventual consistency issues and difficult application logic management.
- ✗
Use a flat structure and duplicate parent information in every child document.
Why it's wrong here
This approach introduces data redundancy and makes updates difficult. If the parent information changes, you would need to update every child document, risking data inconsistency. This is known as the 'denormalization trap' and should be avoided in favor of more structured embedding or referencing patterns depending on scale.
About these practice questions
One of 259 original C100DEV practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.