C100DEV Quick Quiz — 10 Questions

Question 1

What happens if a document grows beyond the 16MB limit due to an 'Embedding' design choice?

Accepted Answer

The write operation will fail.. When a document exceeds 16MB, the MongoDB write operation fails, and the database returns an error. The BSON document size limit is a hard constraint in MongoDB. This scenario highlights why 'unbounded array' design patterns must be avoided. When designing schemas, developers must ensure that embedded data sets have a predictable upper bound, or they must switch to a referencing strategy to maintain the integrity of their data storage.

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MongoDB automatically moves the overflow data to a new document.

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The engine automatically compresses the document to fit.

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The document is split into two, and the original ID is shared.

Question 2

What is the primary advantage of the Subset pattern in MongoDB data modeling?

Accepted Answer

It keeps the working set small to improve cache performance.. The Subset pattern allows you to store the most frequently accessed data in the main document while offloading the rest to another collection. This ensures that the primary document remains small and fits efficiently into the WiredTiger cache, which significantly boosts read performance for common queries. By reducing the overall document size, you also avoid the 16MB BSON limit while still keeping essential information easily accessible for the application's most frequent use cases.

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It eliminates the need for indexes on the collection.

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It automatically scales the database across sharded clusters.

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It prevents data duplication across different collections.

Question 3

Which tool is primarily recommended for performing binary backups and restores of a MongoDB deployment?

Accepted Answer

mongodump. mongodump and mongorestore are standard utilities for creating and restoring binary backups of MongoDB databases. Unlike mongosh which is for interaction, or Compass for visualization, these tools operate directly on the BSON data representation. Understanding the correct backup utility is essential for disaster recovery and data migration, ensuring that the integrity of the data is maintained during the backup process across different MongoDB versions.

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mongosh

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Compass

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mongostat

Question 4

When modeling with MongoDB, why should you avoid 'unbounded' growth in an array field?

Accepted Answer

Because it can lead to hitting the 16MB document limit.. Unbounded arrays are a dangerous anti-pattern because every document has a hard 16MB size limit. As an array grows, the document size increases, eventually causing write operations to fail. Furthermore, updating large documents forces the database to relocate the entire object on disk, which creates significant performance overhead. By limiting array growth, you ensure the database remains stable, performant, and within the physical constraints of the BSON format for all future operations.

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Because it makes the JSON syntax harder to read.

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Because it prevents the use of secondary indexes.

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Because MongoDB does not support arrays as a data type.

Question 5

Which of the following describes the behavior of a 'causally consistent' session in MongoDB?

Accepted Answer

It ensures operations are observed in their logical order.. Causal consistency ensures that operations within a session reflect the order in which they were performed. If an application writes a document and then immediately reads it back within the same session, causal consistency guarantees that the read will see the result of that write, even if the read is directed to a secondary node. This is vital for applications requiring linear-like history for user interactions across distributed read/write nodes.

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It forces all reads and writes to the primary node.

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It eliminates the need for replication in the cluster.

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It significantly increases the latency of every write.

Question 6

When designing a schema for a one-to-many relationship, what is the primary factor in deciding between embedding and referencing?

Accepted Answer

The access pattern and the size of the related data.. The primary factor is the access pattern: how often the data is read and whether the related data is needed together. Embedding is ideal when the child data is frequently accessed with the parent. Referencing is necessary when the child data is large, grows unbounded, or is accessed independently. Choosing the right approach ensures that the application maintains optimal performance by minimizing read latency and avoiding document size limits.

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The total number of documents in the collection.

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The specific version of MongoDB being used by the server.

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The hardware specifications of the database server.

Question 7

An e-commerce application stores products with fluctuating attributes. Which data modeling pattern is most effective for handling diverse product schemas while maintaining efficient filtering?

Accepted Answer

Implementing the Attribute Pattern with an array of key-value pairs.. The Attribute Pattern is the standard solution for schemas with heterogeneous fields. By transforming fields into an array of key-value pairs (k: 'color', v: 'red'), you create a uniform structure that allows for single-index coverage across all attributes. This avoids the limitations of sparse indexes and allows the application to query arbitrary attributes without needing to modify the document structure or rebuild large index sets frequently.

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Embedding every possible attribute as a top-level field in the document.

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Storing all product attributes as a single large JSON blob string.

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Creating a separate collection for every individual product category.

Question 8

You are designing a schema for a social media platform where users have an array of 'tags' and an array of 'mentions'. You attempt to create an index on { tags: 1, mentions: 1 }. What is the most likely outcome of this operation?

Accepted Answer

The index build fails with an error stating that a compound index cannot include more than one array.. MongoDB restricts multikey indexes such that a single compound index cannot cover more than one array field. This limitation prevents the index from growing exponentially in size, which would happen if it had to create a cartesian product of every element in both arrays. Developers must choose which array is more critical for query performance or use alternative indexing strategies.

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The index is created successfully and supports queries that filter by both tags and mentions.

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The index is created but only the first element of each array is indexed to save space.

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The index is created but it will only be used if the query provides a specific index hint.

Question 9

Refer to the exhibit. What is the most appropriate next step for an application encountering this error?

Accepted Answer

Abort the transaction and retry the entire operation.. The 'TransientTransactionError' label indicates that the failure occurred due to a temporary issue, such as a network hiccup or a transient server conflict. In accordance with MongoDB best practices, the application must catch this error, perform a rollback if necessary, and then retry the entire transaction from the beginning. This mechanism ensures that the application state remains consistent despite the intermittent nature of the underlying failure.

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Display an error message to the user and stop.

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Retry the individual failed operation only.

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Ignore the error and continue with the next request.

Question 10

You are designing a schema for an e-commerce platform where products have a variable number of attributes like color, size, and material. Which modeling approach provides the best balance of flexibility and query performance for filtering products by these dynamic attributes?

Accepted Answer

Use the Attribute Pattern with an array of key-value subdocuments.. Using the Attribute Pattern is the standard MongoDB practice for handling heterogeneous data fields. By restructuring attributes into an array of subdocuments containing 'k' (key) and 'v' (value) fields, you can create a single multikey index on this array. This allows the database to efficiently query across arbitrary product properties without requiring schema changes or thousands of unique collection indexes as requirements evolve over time.

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Create a separate collection for every unique attribute type.

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Store all attributes as top-level fields in the document.

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Serialize all product attributes into a single binary Large Object field.