C100DEV Drivers, Tools, Transactions, and Search Practice Question
What is the primary purpose of the 'causal consistency' feature provided by MongoDB sessions?
⚠ Common exam trap
Candidates mistake causal consistency for multi-document atomicity or ACID transactions, assuming it prevents concurrent write conflicts instead of managing read-your-own-writes ordering.
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
✓
To ensure that a client reads the results of its own writes.
Causal consistency ensures that operations within a session appear in an order that respects the causal relationship between them, even when reading from secondary nodes. This is vital in distributed systems to prevent 'time-travel' anomalies, such as reading a stale version of a document immediately after an update. By using the operation time, MongoDB guarantees that a user sees their own writes immediately, maintaining a logical progression of data state.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To force all reads and writes to occur on the primary node only.
Why it's wrong here
Causal consistency does not restrict operations to the primary node. It allows reads from secondaries while ensuring that the data returned is at least as recent as the last write performed within that same session, providing a balance between read scaling and data consistency requirements for the end user.
- ✓
To ensure that a client reads the results of its own writes.
Why this is correct
Causal consistency is specifically designed to guarantee that reads reflect the client's prior writes. Without this, a read following a write might return stale data if the read hits a secondary node that has not yet replicated the latest operation, causing confusion and potential logic errors in the application.
- ✗
To improve overall write throughput by batching operations.
Why it's wrong here
Causal consistency is a read-ordering mechanism, not a write optimization feature. Batching writes is a separate performance consideration handled by the driver's write concern and bulk operation APIs. Causal consistency adds a small metadata overhead to ensure ordering, which is unrelated to the raw write throughput of the cluster.
- ✗
To automatically shard data based on access patterns.
Why it's wrong here
Causal consistency has no relation to data sharding or partitioning strategies. Sharding is managed by the cluster configuration and the shard key chosen by the developer. Consistency features are about how the client perceives the data, whereas sharding is about how the data is physically distributed across the cluster nodes.
About these practice questions
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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.