C100DEV Data Modeling Practice Question
An IoT platform ingests sensor readings every second from thousands of devices. The team wants to minimize the number of documents and index entries while still querying by device and time range. Which schema design best matches these requirements?
⚠ Common exam trap
The trap here is treating an index as a way to reduce document count, when indexes add entries rather than consolidating data.
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
✓
Group readings into time-bucketed documents, each holding many readings for a device over a fixed interval.
Bucketing readings into fixed-interval documents per device reduces document count and index footprint while keeping queries by device and time range efficient. Because each bucket is bounded, documents remain small and writes spread across buckets over time. The alternatives either multiply documents or create unbounded arrays that risk the size limit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Group readings into time-bucketed documents, each holding many readings for a device over a fixed interval.
Why this is correct
Time-bucketed documents combine many readings into one document, cutting the number of documents and index entries while preserving queryability by device and time range. The bucket size is bounded, so documents stay well under 16MB. This directly addresses the stated goals of fewer documents, fewer index entries, and efficient range queries.
- ✗
Store one document per reading with fields for deviceId, timestamp, and value.
Why it's wrong here
One document per reading produces an enormous number of small documents and corresponding index entries, which increases storage overhead and write amplification. It does not reduce document or index count, which is the stated goal. This is the straightforward approach the team is trying to avoid.
- ✗
Store each reading as a separate document and rely on a compound index on deviceId and timestamp.
Why it's wrong here
A compound index improves query performance but does not reduce the number of documents or index entries; in fact it adds index entries per reading. The scenario explicitly asks to minimize documents and index entries, which indexing alone cannot achieve. It solves a different problem than the one stated.
- ✗
Store one document per device containing an array of all readings since deployment.
Why it's wrong here
A single unbounded array per device risks hitting the 16MB document limit and makes updates expensive as the array grows. It also concentrates all writes for a device into one document, limiting write scalability. This fails the goal of a maintainable, bounded representation.
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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.