C100DEV Data Modeling Practice Test 2 — 15 Questions

Question 1

Refer to the exhibit. You have a collection where each document contains a 'tags' array. If you frequently query for documents containing specific tags, what is the best way to model and index this data?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use the multikey index created on the tags array.. MongoDB automatically creates a multikey index when you index an array field. This allows the query engine to efficiently find documents that contain any of the elements in the array. This index type is highly performant for tag-based filtering because it maps every element in the array to the document location, enabling rapid searches without scanning every document in the collection.

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Convert the tags array into a comma-separated string.

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Create one index per tag value to ensure performance.

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Store tags in a separate collection and reference them using an object ID.

Question 2

You are designing a schema for a social media platform. A user has a 'profile' document, and they can have thousands of 'followers'. How should you model the follower relationship?

Accepted Answer

Create a separate collection for followers and store references.. Storing thousands of followers in an array within the user profile document is a violation of the unbounded array anti-pattern, as it would cause the document to grow beyond the 16MB limit and result in poor write performance. Referencing the followers in a separate collection, where each follower is a separate document, provides a scalable solution that supports high growth and efficient querying without performance degradation.

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Embed all follower IDs in an array within the user profile document.

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Use the Attribute Pattern to store each follower as a key-value pair.

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Store the followers in a gridFS file to handle the large size.

Question 3

When designing a schema for a time-series dataset, why is it recommended to use the 'Bucket' pattern?

Accepted Answer

It reduces index size and improves read performance by grouping data.. The Bucket pattern groups multiple related documents (like sensor readings) into a single document representing a time window (e.g., one hour). This significantly reduces the total number of documents in the collection, which improves index efficiency and reduces the memory footprint. By minimizing the index size, the database can keep more of the index in RAM, leading to faster query performance for analytical or time-based data retrieval.

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It allows for unlimited document growth without checking the 16MB limit.

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It automatically converts all data to a binary format for faster storage.

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It enables ACID transactions for non-related collections automatically.

Question 4

Refer to the exhibit. The document uses the Attribute Pattern. Why is this model superior for indexing compared to storing specs as a single sub-document `{ RAM: '16GB', CPU: 'i7' }`?

Accepted Answer

It allows one index to cover any attribute query.. By using the Attribute Pattern with an array of key-value pairs, you can create a single multikey index on the 'specs.k' and 'specs.v' fields. This allows you to query any arbitrary attribute efficiently without having to create a separate index for every possible product specification. This approach provides a flexible, performant way to handle highly variable product schemas that would otherwise require hundreds of individual indexes.

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It reduces the document size compared to sub-documents.

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It enforces strict schema validation for every attribute.

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It automatically performs joins between different products.

Question 5

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 6

You have a collection of 'Users' and a collection of 'Groups'. Users can belong to many groups, and groups can have many users. Which modeling approach is most scalable for this many-to-many relationship?

Accepted Answer

Use a separate 'Membership' collection to map users to groups.. In many-to-many relationships, storing arrays of references on both sides (e.g., group_ids in user, user_ids in group) can lead to unbounded arrays if either collection grows large. The most scalable approach is to use a separate 'Membership' collection that stores the mapping between User IDs and Group IDs. This prevents document bloat and allows you to index the relationship in both directions, ensuring optimal performance for all query types.

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Embed all group names in the user document.

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Store all user IDs in the group document.

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Use the Attribute Pattern to list group IDs.

Question 7

Refer to the exhibit. Which MongoDB design pattern is represented here?

Accepted Answer

The Attribute pattern.. This is the Attribute pattern. It is used when you need to store documents where fields vary significantly or when you need to index multiple fields that share a common structure. By transforming key-value pairs into an array of sub-documents, you can create a single multikey index on the 'specs.k' and 'specs.v' fields, allowing for efficient queries across diverse attribute sets that would otherwise require too many individual indexes.

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The Bucket pattern.

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The Extended Reference pattern.

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The Computed pattern.

Question 8

When designing a schema for a blog application, you need to store comments for posts. The comments grow indefinitely. Which approach is most effective for long-term scalability?

Accepted Answer

Use the Hybrid pattern for embedded and referenced comments.. The Hybrid pattern is best here. You embed the most recent comments (e.g., the last 10) directly within the post document to allow for fast initial page loads. Simultaneously, you store older comments in a separate collection with a reference to the post ID. This provides a balance between high-speed access for recent content and the ability to scale indefinitely without ever hitting the BSON size limit.

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Embed all comments in the post document.

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Reference all comments in a separate collection.

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Use the Attribute pattern for every comment.

Question 9

Refer to the exhibit. Which design pattern is used to store 'total_price' in the document?

Accepted Answer

The Computed pattern.. This is the Computed pattern. By storing the final total in the document, the application avoids recalculating it during every read request. This is particularly valuable in heavy read-heavy applications where the cost of performing arithmetic aggregations across multiple documents is significantly higher than the cost of updating a pre-computed value during the initial write operation. It optimizes read performance at the expense of write complexity.

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The Subset pattern.

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The Polymorphic pattern.

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The Schema Versioning pattern.

Question 10

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 11

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 12

You have a document with an unbounded array of data. Which TWO strategies help mitigate the risk of exceeding the 16MB document size limit?

Accepted Answer

Implement the subset pattern to store only the most recent items in the document.. Managing document growth is critical for long-term stability. The 'subset pattern' keeps only the most relevant items embedded, while 'bucketing' groups related items into separate documents to prevent a single document from growing indefinitely. Both techniques ensure that documents remain performant and well below the 16MB threshold, preventing application crashes when arrays grow beyond predicted sizes over time.

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Increase the maximum BSON document size limit in the mongod configuration file.

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Convert the array to a single large string field to save space.

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Disable indexing on all array fields to prevent size expansion.

Question 13

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 14

Which schema pattern is demonstrated in the exhibit provided?

Accepted Answer

The Embedding Pattern.. The exhibit shows an array of sub-documents nested directly within a parent document, which is the definition of the embedding pattern. This pattern is used to keep related data together in one physical storage unit. It is highly efficient for read-heavy applications where the parent user record and their order history are almost always required at the same time by the application layer.

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The Linking Pattern.

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The Polymorphic Pattern.

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The Computed Pattern.

Question 15

Which TWO of the following are benefits of using the Polymorphic Pattern in MongoDB?

Accepted Answer

It allows a single query to retrieve all related items across different types.. The polymorphic pattern allows you to store different document structures in the same collection, which is useful when entities share common fields but have unique characteristics. By keeping these in one collection, you simplify queries that need to target the entire entity set and reduce the need for complex application-level logic to manage multiple collections for similar object types, improving developer productivity and consistency.

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It enforces strict schema validation for all documents within the collection.

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It automatically increases the write throughput of the database cluster.

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It allows for unlimited document size by splitting fields across collections.