C100DEV Practice Test 3 — 10 Questions

Question 1

Which of the following describes the 'schema-flexible' nature of MongoDB?

Accepted Answer

Documents can have different fields, and the structure can change over time.. Schema-flexibility means that documents in the same collection do not need to have the same fields or structure. This allows developers to iterate quickly, as adding new fields or changing data structures doesn't require modifying existing data or taking the database offline. This is a fundamental departure from rigid, table-based relational schemas that mandate defined structures before any data can be inserted.

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All documents must have a predefined structure determined by the first document inserted.

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The database automatically flattens all nested documents upon insertion.

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Schema enforcement can only be achieved by using third-party database proxies.

Question 2

What is the primary risk of using the 'Linking Pattern' improperly?

Accepted Answer

It reduces read performance due to increased $lookup overhead.. The primary risk is creating too many 'joins' (lookups) in the application, which leads to slow performance. When you link data across collections, the database must perform extra work to resolve these references. If done too frequently, especially in high-traffic read paths, it creates significant latency and increases the load on the database, which directly undermines the performance benefits usually associated with MongoDB's document-based architecture.

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It causes data inconsistency.

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It forces the use of fixed schema documents.

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It prevents the use of secondary indexes on the collection.

Question 3

An e-commerce application frequently executes a query filtering by 'category' and 'brand', and then sorts the results by 'price' in ascending order. You created a compound index on { category: 1, brand: 1, price: 1 }. Which statement best describes how MongoDB utilizes this index for a query that filters only on 'brand' and 'price'?

Accepted Answer

MongoDB will perform a collection scan as the query lacks the 'category' prefix required by the index.. Compound indexes in MongoDB utilize a prefix-based matching system. To use an index for filtering or sorting, the query must include the first field of the index or a sequence of fields starting from the first. Since 'category' is the first field and is missing from the query filter, MongoDB cannot use this specific index for efficient lookups, highlighting the importance of field ordering in index design.

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The query engine will perform an index scan because 'brand' and 'price' are part of the index.

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The index will be used only for the sort operation on 'price' but not for the filter on 'brand'.

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MongoDB will use the index to find 'brand' but will still need to fetch documents to verify 'price'.

Question 4

When using MongoDB Compass to analyze query performance, which tool is most effectively used to view the explain plan output in a visual format?

Accepted Answer

The Explain Plan tab.. MongoDB Compass provides a built-in 'Explain Plan' tab that visualizes how the database executes a query. This tool represents the execution path as a tree, highlighting which indexes were used, how many documents were scanned, and the efficiency of the operation. Using this visual output is essential for developers to identify bottlenecks, missing indexes, or suboptimal query structures without manually parsing complex JSON output from the shell.

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The Aggregation Pipeline builder.

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The Schema validation tab.

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The Connection health monitor.

Question 5

You have a collection of user activity logs where queries frequently filter by `userId` and sort by `timestamp` in descending order. Which single index definition provides optimal support for this query pattern without requiring an in-memory sort?

Accepted Answer

{ userId: 1, timestamp: -1 }. To avoid an in-memory sort, the index direction must match the query's sort direction for the sorted fields. Since `userId` is filtered via equality and `timestamp` is sorted descending, the index `{ userId: 1, timestamp: -1 }` allows MongoDB to traverse the index naturally in the required order, ensuring optimal query performance and preventing sorting memory limit errors during execution.

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{ userId: 1, timestamp: 1 }

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{ timestamp: -1, userId: 1 }

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{ userId: -1, timestamp: -1 }

Question 6

Which stage should be placed first in an aggregation pipeline to optimize performance when filtering a large collection based on an indexed field?

Accepted Answer

$match. The $match stage filters documents before they pass to subsequent stages, significantly reducing the data volume processed by memory-intensive operations like $sort or $group. Placing it first allows MongoDB to utilize indexes, which minimizes disk I/O and RAM usage. Efficient pipeline construction is critical for scalability in production environments, as reducing the input set early directly impacts the latency and resource consumption of the entire aggregation operation.

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$project

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$group

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$sort

Question 7

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 8

What is the primary benefit of using $unionWith to combine data from two collections in a single pipeline?

Accepted Answer

It allows querying multiple collections in one pipeline.. $unionWith allows developers to append results from one collection to another within a single aggregation pipeline. This provides a mechanism for performing multi-collection queries without the overhead of multiple application-level calls. It simplifies data retrieval for scenarios like merging similar data from sharded collections or combining logs from multiple time-based collections, which is a powerful technique for efficient data consolidation and reporting.

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It automatically joins documents based on a common ID.

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It is faster than $match because it avoids indexes.

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It replaces the need for a $group stage entirely.

Question 9

A developer is working with a MongoDB collection that stores sensor readings. Each document contains a timestamp, sensor ID, and a nested object 'readings' with fields like 'temperature', 'humidity', and 'pressure'. The developer needs to query for all documents where the temperature reading exceeds 30 degrees Celsius. Which query correctly retrieves these documents?

Accepted Answer

db.sensors.find( { "readings.temperature": { $gt: 30 } } ). MongoDB uses dot notation to query nested fields within documents. The query with "readings.temperature" and $gt correctly targets the nested temperature value. The other options either misinterpret the structure, use array-specific operators incorrectly, or rely on inefficient JavaScript evaluation, making them unsuitable for this scenario.

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db.sensors.find( { $where: "this.readings.temperature > 30" } )

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db.sensors.find( { readings: { temperature: { $gt: 30 } } } )

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db.sensors.find( { "readings": { $elemMatch: { temperature: { $gt: 30 } } } } )

Question 10

A developer is reviewing the characteristics of MongoDB documents. Which two of the following statements are accurate? (Choose two.)

Accepted Answer

Each document must have a unique _id field, which can be of any BSON data type except an array.. The maximum BSON document size is 16 MB, and every document must have a unique _id field that can be any BSON type except an array. Documents in a collection need not share the same fields, they are stored in BSON not JSON, and field names cannot contain null characters.

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Documents are stored in a human-readable JSON format on disk.

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Field names in a document can contain the null character.

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Documents in the same collection must have the same set of fields.