C100DEV Practice Test 18 — 15 Questions

Question 1

When should you use the 'Computed Pattern' in MongoDB?

Accepted Answer

When the data is accessed frequently and requires an expensive calculation.. The Computed Pattern is used to store calculated values (like totals, counts, or averages) directly within a document to minimize expensive real-time calculations. This is vital when the cost of computing a result exceeds the cost of updating a stored value. It is particularly effective for read-intensive workloads where the computed result is requested frequently, allowing the application to simply read the pre-calculated value from the document.

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When you have a high write-to-read ratio for the specific data field.

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When you need to store binary large objects (BLOBs) in the database.

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When you want to prevent all indexing on a specific collection.

Question 2

Which document design pattern should be used to store a one-to-many relationship where the child documents are frequently accessed together with the parent?

Accepted Answer

Embed child documents directly as an array within the parent document.. The embedding pattern is preferred for one-to-many relationships where children are always retrieved alongside the parent. This pattern minimizes the number of required read operations by ensuring all necessary data is located within a single document. This reduces latency and eliminates the need for expensive application-level joins, resulting in highly performant read operations for typical read-heavy application workflows.

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Normalize the data by creating a separate collection for child documents.

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Store the child data in a separate database to isolate the write load.

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Use a flat structure and duplicate parent information in every child document.

Question 3

Refer to the exhibit. If a query is executed as db.collection.find({ "metadata.type": "book" }), how will MongoDB use the created index?

Accepted Answer

MongoDB will perform a collection scan because the index is on the whole object.. Indexing a whole subdocument (the 'metadata' field) creates an index on the literal BSON object. This index is only useful for exact matches of the entire subdocument, including field order. It cannot be used to look up individual fields within that subdocument using dot notation. To index internal fields, you must use the dot notation in the index definition itself.

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The index will be used to quickly find all documents where 'type' is 'book'.

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The index will be used, but performance will be slower than a dot-notation index.

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The index build will fail because metadata is a nested document.

Question 4

Which TWO of the following statements regarding the ESR rule for index creation are true?

Accepted Answer

Equality fields should be placed before sort fields.. The ESR rule stands for Equality, Sort, and Range, providing a standard guideline for ordering fields in a compound index to maximize performance. By placing equality fields first, the query engine narrows down the search space immediately. Sorting fields come next to satisfy the query's order requirements without blocking, and range fields are placed last to allow the index to effectively filter remaining documents using the index prefix.

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Equality fields should be placed after range fields.

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The ESR rule is only applicable to single-field indexes.

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Range fields should always be placed at the beginning of an index.

Question 5

When designing a document model, which factor is most important for determining whether to embed or reference data?

Accepted Answer

The application's data access patterns.. The access pattern—specifically how often data is retrieved and how it is used—is the deciding factor. If data is usually needed together, embedding provides the best read performance. If data is accessed independently or the child data grows without bound, referencing is more appropriate. Aligning the model with access patterns is the core tenet of MongoDB performance optimization and design.

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The total number of documents in the collection.

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The memory limit of the database server.

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The complexity of the data's object-oriented relationships.

Question 6

What is the primary role of the 'mongos' process in a sharded cluster?

Accepted Answer

It routes requests to the correct shards.. The 'mongos' process acts as a query router, bridging the gap between client applications and the sharded cluster. It receives incoming requests, determines which shards contain the necessary data using the cluster's metadata, and routes the request to the appropriate shards. It then aggregates the results before returning them to the client. This abstraction allows applications to interact with the entire cluster as if it were a single database instance.

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It stores the data chunks for the shards.

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It manages the election of the primary node.

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It persists the metadata for the entire cluster.

Question 7

You are storing GPS coordinates in a 'locations' collection using GeoJSON format. You need to perform queries that find points within a specific polygon. Which index type must be created on the location field?

Accepted Answer

A '2dsphere' index to support GeoJSON and spherical geometry.. For querying data on a sphere (like the Earth), MongoDB provides the '2dsphere' index. This index type supports GeoJSON objects and calculates distances using spherical geometry. It is required for advanced geospatial queries such as $geoWithin or $nearSphere when working with real-world coordinates and complex shapes like polygons.

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A '2d' index, as it is the standard for all geospatial data.

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A standard ascending index, as GeoJSON is just a nested document.

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A 'geo' index, which automatically chooses between 2d and 2dsphere.

Question 8

Which of the following describes the behavior of the BSON format within MongoDB?

Accepted Answer

It is a binary-encoded serialization of JSON documents.. BSON (Binary JSON) is the primary data storage format for MongoDB. It extends JSON by providing additional types like Date and BinData while being optimized for fast traversal and efficient serialization. Understanding BSON is critical because it dictates how data is physically stored on disk and read into memory, ensuring that MongoDB maintains its high-performance characteristics while handling complex data structures.

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It is a text-based format identical to JSON but with limited character support.

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It supports a subset of data types compared to standard JSON.

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It requires manual conversion by the driver for every read operation.

Question 9

Why does using a $limit stage immediately after a $sort stage provide a performance advantage?

Accepted Answer

It allows the engine to keep only the top results in memory.. When $limit follows $sort, MongoDB uses a 'top-K' algorithm to maintain only the top N results in memory throughout the sort process. This prevents the database from needing to sort the entire collection in RAM, as it can discard documents that fall outside the sorted top-K range. This optimization is crucial for large datasets where sorting the full collection would exceed memory limits or significantly impact overall system throughput.

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It forces the use of a clustered index.

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It automatically creates a temporary index.

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It disables the need for sorting entirely.

Question 10

Refer to the exhibit. Why is this pipeline considered suboptimal?

Accepted Answer

The $match stage should come before $project.. The pipeline is suboptimal because it performs projection before filtering. By projecting fields first, the database processes every document in the collection. If the $match stage were moved to the beginning, the database could potentially use an index to filter documents before performing the field projection, reducing the amount of data the projection logic needs to handle, thus improving overall performance.

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The $project stage is too slow.

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The _id: 0 syntax is invalid.

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$match cannot follow $project.

Question 11

In the context of the MongoDB Node.js driver, what happens if an application fails to provide a write concern?

Accepted Answer

The driver applies the default write concern (usually 'w: 1').. If no write concern is explicitly defined, the driver uses the default write concern configured at the client or database level. If no default is configured, the driver falls back to 'w: 1', meaning the operation is considered successful once the primary node receives and acknowledges the write. This is the standard behavior to balance performance with a baseline level of data durability in most production applications.

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The write will never be acknowledged by the server.

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The server automatically sets the write concern to 'majority'.

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The write will be rejected by the MongoDB server.

Question 12

An application uses a multi-document transaction to update inventory and process an order. What happens if the transaction exceeds the default transaction lifetime limit of 60 seconds?

Accepted Answer

The server automatically aborts the transaction and returns an error to the client.. Transactions in MongoDB are designed to be short-lived to prevent performance degradation and memory pressure. If a transaction stays open longer than the transactionLifetimeLimitSeconds configuration, the reaper process marks it as aborted. This ensures that locking contention is minimized, as prolonged transactions hold locks on documents, preventing other operations from proceeding and potentially leading to high latency or deadlocks across the cluster.

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The transaction automatically commits partial changes to ensure data availability.

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The client receives a warning, but the transaction continues until completion.

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The database increases the transaction lifetime limit dynamically to prevent failure.

Question 13

When should a TTL (Time-To-Live) index be used in a MongoDB collection?

Accepted Answer

To manage temporary or ephemeral data.. TTL indexes are designed to automatically remove documents from a collection after a certain period of time or at a specific time. This is ideal for ephemeral data like session logs, cached items, or temporary alerts. By relying on the database engine to handle the deletion, developers avoid writing custom cleanup scripts, ensuring that data is purged consistently without impacting application logic or manual overhead on the system.

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To keep data indefinitely.

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To speed up high-frequency write operations.

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To ensure unique values in a field.

Question 14

When creating an index on a large collection in a production environment, what is the primary advantage of using the 'background' option (for versions prior to 4.2) or the standard build process in 4.2+?

Accepted Answer

It prevents the index build from blocking read and write operations on the collection.. In modern MongoDB (4.2+), all index builds use an optimized process that only holds an exclusive lock at the beginning and end of the operation, allowing the database to remain available for reads and writes. This prevents the index creation from blocking application traffic, which is essential for maintaining high availability and a good user experience during maintenance.

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The index build completes much faster than a foreground build.

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The index will take up significantly less space on the disk.

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It automatically creates the index on all secondaries in a single transaction.

Question 15

What is the primary function of the MongoDB connection pool in a driver?

Accepted Answer

To reduce the latency of opening new connections.. A connection pool maintains a cache of open connections to the database server. Reopening a connection for every request is expensive due to the overhead of TCP handshakes and authentication. By reusing existing connections, the driver significantly reduces latency and resource consumption, allowing the application to handle high volumes of traffic more efficiently and providing a more stable connection to the database layer.

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To encrypt data in transit between the client and the server.

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To automatically shard data based on the client's request.

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To store query results in memory for faster retrieval.