MongoDB Change Streams and Real-Time Updates Quiz Quiz

Assess your understanding of change streams and real-time data updates in MongoDB. Explore fundamental concepts, triggers, and practical scenarios relevant to modern database monitoring and live applications.

1. 1. Basic Concept of Change Streams

   What is the primary purpose of enabling change streams in a MongoDB database?

   1. To compress database storage
   2. To permanently delete documents from collections
   3. To monitor and react to real-time changes in data collections
   4. To perform automated schema migrations

   Explanation: Change streams provide a mechanism to observe real-time changes in collections, enabling live updates for applications. They do not delete documents, so 'To permanently delete documents from collections' is incorrect. Change streams do not handle automated schema migrations directly, making that option irrelevant. Compressing database storage is unrelated to the use of change streams.

2. 2. Operations Detected by Change Streams

   Which of the following operations can trigger a change event in a change stream watching a collection?

   1. Backup creation
   2. Export data
   3. Insert, update, delete
   4. Only read operations

   Explanation: Change streams are activated by insert, update, or delete operations on monitored data. Read operations do not modify data, so they do not generate change events. Exporting data and backup creation are maintenance or administrative tasks and do not affect the tracked changes in real time.

3. 3. Change Stream Output

   When a change occurs and is captured by a change stream, how is the information typically delivered to the application?

   1. As a change event document
   2. By exporting to a CSV file
   3. In a compressed log file
   4. Through graphical notifications only

   Explanation: Change streams send events as change event documents, which contain details about the operation. Compressed log files and CSV exports are not direct outputs of change streams. Graphical notifications are not a built-in delivery format for programmatically accessing stream data.

4. 4. Restricting Change Streams

   Which method can be used to receive only specific types of events from a change stream, such as just updates?

   1. Enabling query logging
   2. Compressing the collection
   3. Applying a pipeline with $match to filter events
   4. Disabling write operations

   Explanation: You can use aggregation pipelines with the $match stage to filter for specific change types. Query logging does not filter change stream events, nor does compressing the collection or disabling writes, which would instead prevent or control data changes at a lower level.

5. 5. Real-Time Application Example

   If you are building a real-time notification system that alerts users to data updates, which MongoDB feature would be most suitable for detecting those updates as they happen?

   1. Aggregation pipelines without streaming
   2. Indexing
   3. Change streams
   4. Replica sets

   Explanation: Change streams are specifically designed for real-time detection of data changes. Indexing improves query performance but does not notify about dataset updates. Aggregation pipelines without streaming process data but do not offer live updates. Replica sets provide redundancy, not change detection.

6. 6. Collection and Database-Level Change Streams

   Can change streams be opened at the database level to monitor all collections, and what is required for this functionality?

   1. Yes, if all collections are in a replica set or sharded cluster
   2. Yes, but only on standalone servers
   3. No, only single collections can be monitored
   4. No, change streams are not supported at the database level

   Explanation: Change streams at the database or cluster level are supported provided all collections are part of a replica set or sharded cluster setup. Attempting this on standalone servers is not supported. The statement that only single collections can be watched is outdated for these configurations, and the answer about lack of support is incorrect based on modern setups.

7. 7. Resume Tokens

   What is the purpose of a resume token in the context of MongoDB change streams?

   1. They optimize search queries
   2. They allow resuming the change stream from a specific event after interruptions
   3. They are used to limit the size of documents
   4. They compress network transmissions

   Explanation: Resume tokens are included in change stream events so applications can restart the stream from a known point. They do not optimize queries, limit document size, or compress data transmissions; each of those are responsibilities of different database features.

8. 8. Prerequisites for Using Change Streams

   Which MongoDB setup is required for enabling change streams on a collection?

   1. An archived database
   2. A standalone server
   3. A CSV-imported collection
   4. A replica set or a sharded cluster

   Explanation: Change streams are only supported on deployments using replica sets or sharded clusters. Standalone servers do not provide this functionality. CSV-importing or archiving databases does not impact native support for change streams.

9. 9. Event Types in Change Streams

   Which of the following is NOT a valid change event type captured by change streams?

   1. Index creation
   2. Update
   3. Delete
   4. Insert

   Explanation: Change streams capture document-level changes such as insert, update, and delete but do not monitor schema or index creation events. 'Index creation' is therefore the correct answer, while the other options represent valid event types.

10. 10. Latency of Change Streams

    Which statement best describes the latency of change streams in delivering real-time updates to applications?

    1. Updates are delivered with minimal latency, close to instant
    2. Only daily batch updates are sent
    3. Updates are always delayed by several hours
    4. Events are aggregated and sent once a week

    Explanation: Change streams provide updates in near real-time, making them highly responsive for live applications. Several-hour delays, daily batches, or weekly aggregation are not characteristics of this feature and would fail to deliver on real-time requirements.