C100DEV Practice Test 2 — 10 Questions

Question 1

Which TWO of the following scenarios are best suited for using the 'Subset Pattern' in MongoDB?

Accepted Answer

A blog post collection where you display the last 5 comments on the main view.. The Subset Pattern is used to prevent documents from growing too large while keeping frequently accessed data local. By embedding only the most important subset of child documents (e.g., the five most recent comments) and referencing the rest, you balance read performance with document size management. This is essential for maintaining consistent performance levels as datasets scale, ensuring that the working set remains efficient for typical user queries.

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A shopping cart collection where items are added and removed constantly.

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A product catalog where you need to filter by over 100 different categories.

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An audit log collection where every action must be stored for compliance.

Question 2

You are processing a large collection of user sessions. You need to calculate the average duration of sessions grouped by 'userId', but you must filter out any sessions lasting less than 5 seconds before performing the grouping. Which stage order is most efficient?

Accepted Answer

$match then $group. Filtering data as early as possible in an aggregation pipeline is a fundamental optimization technique. By placing the $match stage before the $group stage, you significantly reduce the number of documents that need to be processed by the subsequent grouping operation. This reduces memory usage and improves overall pipeline performance by limiting the dataset size early on, which is critical when working with large-scale production collections.

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$group then $match

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$project then $match then $group

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$group then $project then $match

Question 3

What is the primary consequence of having too many indexes on a collection that experiences a high volume of write operations?

Accepted Answer

Decreased performance of write operations.. Every index on a collection must be updated whenever a document is inserted, updated, or deleted. In write-heavy workloads, this results in significant write amplification. Each write operation triggers multiple index update tasks, consuming CPU and I/O resources. Maintaining excessive indexes leads to performance degradation as the system struggles to keep all indexes synchronized with the base data, directly impacting latency and throughput for application-level database interactions.

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Increased disk space usage for data storage.

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Increased memory usage for query execution.

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Improved speed for data deletion operations.

Question 4

Which of the following describes the 'Extended Reference' pattern and its primary use case?

Accepted Answer

Copying frequently accessed fields to a referenced document to minimize joins.. The Extended Reference pattern involves copying frequently accessed fields from a referenced document into the current document. This eliminates the need for costly $lookup operations during read queries. It is a vital pattern for high-performance applications where latency is critical, as it trades a small amount of data redundancy for significant gains in read speed by enabling single-document retrieval for complex data views.

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Embedding all child documents to ensure total data atomicity.

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Storing the entire child document inside the parent for faster retrieval.

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Using an array of document IDs to link parent and child documents.

Question 5

Which of the following best describes the purpose of the MongoDB 'mongosh' shell?

Accepted Answer

To serve as a modern interactive JavaScript shell.. The 'mongosh' shell is the modern, interactive command-line interface for MongoDB. It allows developers to run JavaScript commands to interact with the database, perform CRUD operations, manage indexes, and execute administrative tasks. It replaces the legacy 'mongo' shell, offering improved features like syntax highlighting, auto-completion, and better integration with current MongoDB server versions, making it the essential tool for daily development and database management tasks.

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To act as a GUI for database administration.

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To perform background replication between nodes.

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To automatically optimize database query indexes.

Question 6

An e-commerce application models product reviews inside an array on the product document. As popular products accumulate hundreds of thousands of reviews, write operations and document fetch operations experience performance degradation. Which data modeling pattern best resolves this issue?

Accepted Answer

Implement the Bucket Pattern by storing a limited number of reviews per document alongside grouping metadata, creating new bucket documents as needed.. Migrating from an unbounded embedded array to the Bucket Pattern divides reviews into manageable document chunks based on a count or time threshold. This prevents documents from exceeding the 16MB limit and reduces RAM pressure. Managing growth ensures predictable update performance and maintains indexing efficiency across high-throughput collections in production workloads.

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Keep the array embedded but add a partial index to speed up searches on the most recent reviews inside the array.

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Move every review into its own separate collection and establish a manual join reference back to the parent product ID.

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Convert the reviews array into a capped collection and use tailing cursors to push updates directly to the product document.

Question 7

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?

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 8

What is the primary benefit of the 'Bucket Pattern' in MongoDB data modeling?

Accepted Answer

It keeps document sizes within reasonable limits for time-series data.. The Bucket Pattern is used to avoid the 'unbounded array' problem, where a single document could grow indefinitely. By grouping data into manageable 'buckets' (e.g., one document per day of sensor data), the pattern keeps document sizes manageable, ensures efficient indexing, and optimizes write performance. This approach prevents documents from hitting the 16MB limit while maintaining query speed for time-series data.

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It eliminates the need for any indexing on the collection.

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It forces data normalization across multiple sharded clusters.

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It automatically migrates old data to archival storage systems.

Question 9

Which TWO of the following scenarios are optimal for using the Embedding pattern instead of Referencing?

Accepted Answer

A user document containing the user's primary shipping address.. Embedding is preferred when data is accessed together or exhibits a 'contains' relationship, minimizing round trips. It is ideal for one-to-few relationships. Conversely, Referencing is better for large, unbounded datasets or many-to-many relationships. Understanding these trade-offs is critical for MongoDB developers to minimize I/O and maximize the efficiency of read operations, as embedded documents are retrieved in a single read operation by the database engine.

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A blog post containing a list of comments that grows indefinitely.

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A product catalog with millions of items linked to various categories.

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A sensor data stream that stores millions of readings per hour.

Question 10

Which operator is used to calculate the average value of a numeric field in a $group stage?

Accepted Answer

$avg. The $avg accumulator operator is used to compute the mean of all numerical values for a specific field across a group of documents. It is a fundamental tool for data analysis and reporting. Knowing how to use accumulators within the $group stage is essential for generating meaningful insights from raw data, which is a primary use case for the MongoDB Aggregation Framework.

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$mean

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$sum

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$median