C100DEV Practice Test 19 — 15 Questions

Question 1

An e-commerce application stores products in a collection. Each product has a variable number of attributes, such as 'size', 'color', or 'material'. Which data modeling approach best leverages MongoDB's flexible schema while maintaining query efficiency?

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

Accepted Answer

Embed variable attributes directly within each product document.. Using a polymorphic schema with optional fields directly in the document is the canonical MongoDB approach. This strategy avoids the JOIN operations required by relational databases when attributes are stored in separate tables. By keeping data together, developers achieve high performance for read operations and take full advantage of MongoDB's indexing capabilities on specific document fields, regardless of whether every document contains that field.

Answer

Normalize all attributes into a secondary 'attributes' collection linked by ProductID.

Answer

Implement a fixed schema using a single large object containing every possible attribute as a null value.

Answer

Serialize the entire attribute set into a single base64-encoded string field.

Question 2

What is the primary benefit of using a partial index in MongoDB?

Accepted Answer

It reduces the index size and resource usage.. Partial indexes allow you to index only a subset of documents that meet a specific filter expression. This reduces the index size, resulting in lower memory and storage consumption. By ignoring unnecessary documents, the database avoids wasting resources on indexing data that is never or rarely queried. This optimization is particularly beneficial for large collections where only a small percentage of data is active or frequently accessed, leading to significant performance gains.

Answer

It forces all documents to be indexed.

Answer

It prevents duplicates across the entire collection.

Answer

It is required for all compound indexes.

Question 3

What is the primary benefit of the 'Extended Reference Pattern'?

Accepted Answer

It improves read performance by reducing the need for joins.. The Extended Reference Pattern involves copying a small amount of frequently accessed data from a referenced document into the primary document. This allows the application to retrieve the most critical information in a single read, avoiding a join for the majority of use cases. It optimizes for the common case while still allowing the full referenced document to be fetched only when truly needed.

Answer

It completely eliminates the need for any referencing in the database.

Answer

It allows documents to grow indefinitely without size limits.

Answer

It ensures that all data across all collections remains perfectly synchronized.

Question 4

What happens if an aggregation stage exceeds the 100MB memory limit and 'allowDiskUse' is set to false?

Accepted Answer

The aggregation operation fails and returns an error.. By default, MongoDB limits each aggregation stage to 100MB of RAM to prevent a single query from consuming excessive system resources and affecting database stability. If a stage exceeds this limit without explicit permission to write to temporary files on disk, the query will fail with an error. Developers must proactively optimize pipelines to minimize memory footprint or enable disk use when large-scale processing is required.

Answer

The pipeline execution stalls until memory is freed.

Answer

The database automatically increases the limit.

Answer

The pipeline skips the stage and continues.

Question 5

Which aggregation operator is most appropriate for counting the total number of documents in a collection?

Accepted Answer

$count. The $count stage is a specialized aggregation stage that returns a document containing the number of input documents at that point in the pipeline. It is shorthand for a $group stage with a $sum accumulator. Using $count is more readable and concise than building a manual $group stage, making it the best practice for simple document counting tasks within an aggregation pipeline.

Answer

$group with $sum

Answer

$limit

Answer

$match

Question 6

Which MongoDB tool should be used to perform a point-in-time backup of a MongoDB cluster while the database is running?

Accepted Answer

mongodump. mongodump is the standard command-line utility for creating binary exports of MongoDB data. When used with the --oplog option, it allows for point-in-time recovery by capturing the operations log during the backup process. This is essential for maintaining data integrity in production environments where downtime must be avoided, allowing administrators to restore the cluster to a specific state as needed by business requirements.

Answer

mongoimport

Answer

mongostat

Answer

mongorestore

Question 7

Which TWO of the following are valid accumulators that can be used within a $group stage?

Accepted Answer

$sum. The $group stage relies on accumulator expressions to perform calculations across a group of documents. Understanding which operators are available is vital for creating summary reports. $sum and $avg are standard accumulators that allow developers to perform arithmetic operations on grouped data, enabling complex analytical queries that reveal trends and metrics directly from the database layer without requiring application-side processing.

Answer

$match

Answer

$project

Answer

$sort

Question 8

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.

Answer

Embed all group names in the user document.

Answer

Store all user IDs in the group document.

Answer

Use the Attribute Pattern to list group IDs.

Question 9

Which stage is used to change the order of documents in an aggregation pipeline?

Accepted Answer

$sort. The $sort stage is the standard operator for ordering the documents in a pipeline based on specified fields. It supports both ascending and descending order. Proper sorting is a requirement for many other stages, such as $limit or $group, when those stages rely on the order of input data to produce correct or expected results in a deterministic manner.

Answer

$order

Answer

$match

Answer

$project

Question 10

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.

Answer

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

Answer

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

Answer

Disable indexing on all array fields to prevent size expansion.

Question 11

Which TWO of the following are common use cases for using the 'mongosh' shell in a production environment?

Accepted Answer

Querying and troubleshooting live collection data.. mongosh is the modern shell for MongoDB, offering a rich environment for database administration and scripting. Beyond simple CRUD, it is essential for checking cluster health, managing indexes, and performing emergency administrative tasks directly on the server. Because it provides full access to the MongoDB driver API, it is an indispensable tool for developers and DBAs to troubleshoot production issues and perform ad-hoc data investigations effectively.

Answer

Developing the entirety of a high-concurrency Node.js application.

Answer

Replacing the driver for all production database traffic.

Answer

Automatically scaling the number of nodes in an Atlas cluster.

Question 12

Which approach is best for handling a 'One-to-Squillions' relationship in MongoDB?

Accepted Answer

Store the parent ID in the child documents.. For a 'One-to-Squillions' relationship, where a parent has a massive, unbounded number of children, neither embedding nor an array of references is viable. Storing the parent ID in the child documents (the 'referencing' pattern) is the only scalable solution. This allows the child documents to be queried efficiently by the parent ID without hitting document size limits or requiring massive updates to the parent document during growth.

Answer

Embed all child documents inside the parent document.

Answer

Store an array of child IDs in the parent document.

Answer

Use a separate collection for each child to ensure isolation.

Question 13

What is the primary technical limitation when creating a covered query?

Accepted Answer

All projected fields must be contained in the index.. A covered query occurs when all fields requested in the query are present in the index. This allows MongoDB to return the result set directly from the index without having to load the actual documents from disk. The primary limitation is that you must include only the indexed fields in your projection. If even one field is not in the index, the database must perform an expensive 'fetch' operation to retrieve the full document from storage.

Answer

The query must include the _id field.

Answer

The collection must be small.

Answer

Covered queries only work with single-field indexes.

Question 14

Refer to the exhibit. Which stage should be used to transform this document into three separate documents, one for each tag?

Accepted Answer

$unwind. The $unwind stage is designed specifically to deconstruct an array field from the input documents to output a document for each element. This is essential for operations like counting occurrences of individual array elements or calculating statistics across specific array items. Without $unwind, the $group stage would treat the entire array as a single value rather than individual components, preventing granular analysis of the array contents.

Answer

$group

Answer

$project

Answer

$match

Question 15

An application needs to store an e-commerce catalog where products have varying attributes such as clothing sizes or electronic specifications. You choose MongoDB for its dynamic schema. Which design approach best leverages the MongoDB document model for this use case?

Accepted Answer

Embed the varying attributes directly within each product document using flexible subdocuments or arrays that match the specific item.. Storing variable attributes directly inside the document using flexible subdocuments or arrays allows efficient single-query retrieval of all product details. This harnesses the core advantage of the document model by keeping related data together, avoiding complex multi-table joins and matching the natural data access patterns of modern applications.

Answer

Normalize product attributes into multiple separate collections linked by manual application references to enforce rigid schema constraints.

Answer

Serialize all product attributes into a single large binary blob string stored in a single generic text field inside the main document.

Answer

Enforce a strict static schema across all product documents using server-side validation rules that require every possible attribute key to be present.