C100DEV Practice Exam 2 — 20 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.

Answer

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'.

Answer

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.

Answer

{ 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.

Answer

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

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

Answer

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.

Answer

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.

Question 11

A developer is working with a MongoDB collection that stores product data. The collection has documents with a field 'price' that is sometimes stored as a double and sometimes as a string, due to inconsistent data entry. A query is run to find all products with a price greater than 100. What is the expected behavior of this query?

Accepted Answer

The query will return only documents where 'price' is a double greater than 100.. MongoDB uses type bracketing for comparison operators: only values of the same BSON type as the operand are considered. Since 100 is a double, only double prices greater than 100 are returned. Strings are not converted, and no error occurs. This underscores the importance of consistent data types in a collection.

Answer

The query will return an error because of the mixed data types in the 'price' field.

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The query will return documents where 'price' is a string greater than '100' lexicographically, as well as doubles greater than 100.

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The query will return all documents where 'price' is greater than 100, regardless of whether it is a double or string.

Question 12

An application stores customer profiles in a MongoDB collection. A developer notices that documents created by different services have inconsistent shapes: some use a string for phone, others an array of strings, and some omit the field entirely. The team wants to enforce a consistent structure for new writes while still allowing existing documents to remain. Which MongoDB feature should they use?

Accepted Answer

A JSON Schema validator attached to the collection with the $jsonSchema operator and validationLevel set to strict.. MongoDB supports schema validation through the $jsonSchema operator, configured on a collection with collMod. Setting validationLevel to strict applies the rules to all inserts and updates, so new writes must match the declared structure. Existing documents that are not modified remain untouched, which satisfies the requirement to leave current data as is while enforcing consistency going forward.

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A unique index on the phone field, which forces every document to use the same BSON type for that field.

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A partial index with a filter expression that excludes documents lacking the phone field.

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A change stream that watches the collection and rejects documents whose shape differs from a template.

Question 13

An application using the MongoDB Node.js driver must ensure that a multi-document transaction is retried correctly when it encounters a transient error. Which two practices are required? (Choose two.)

Accepted Answer

On a commit error with the UnknownTransactionCommitResult label, retry the commit operation.. The Node.js driver exposes error labels that tell the application how to recover. A TransientTransactionError requires restarting the entire transaction, while UnknownTransactionCommitResult requires retrying the commit. These two behaviors are the documented retry pattern for multi-document transactions. Other options either misplace read concern, misunderstand the lifetime limit or weaken durability without addressing retries.

Answer

Increase the transaction lifetime limit beyond 60 seconds to avoid all retries.

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Set the readConcern to 'snapshot' on every individual operation inside the transaction.

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Disable write concern to make commits faster and reduce retries.

Question 14

A development team is modeling a MongoDB collection for a ticketing system. Each ticket has a unique ticketId, a status, and a variable set of custom fields that differ by department. The team wants to ensure that queries on ticketId and status can use indexes efficiently and that the custom fields remain flexible. Which statement best describes how the document model supports this requirement?

Accepted Answer

Documents in the same collection can have different fields, and indexes on ticketId and status can be created regardless of the custom fields present.. The document model allows each ticket to have its own shape while sharing common fields. Indexes are created on specific fields such as ticketId and status, and they work even when documents contain additional custom fields. This gives the team efficient lookups on the common fields and the flexibility to store department-specific data without schema migrations.

Answer

MongoDB enforces a collection-level schema, so the team must define all custom fields in a validator before inserting tickets.

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Custom fields must be stored in a separate collection because MongoDB does not allow arrays or subdocuments in the same collection as scalar fields.

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Indexes in MongoDB are created on collections, not fields, so the team cannot index ticketId and status without also indexing all custom fields.

Question 15

A developer needs to create an Atlas Search index that supports autocomplete on a `name` field. Which Atlas Search index definition is required?

Accepted Answer

An index with a field of type 'autocomplete' and an analyzer such as 'lucene.standard' or a custom edge-gram analyzer.. Atlas Search uses the `autocomplete` field type to support prefix suggestions. The field must be configured with an analyzer that produces edge-grams or n-grams, such as a custom analyzer using the edgeGram tokenizer. Standard string or text indexes do not provide this behavior. Wildcard indexes are unrelated to Atlas Search and cannot satisfy the autocomplete requirement.

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A text index with the default language analyzer.

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A standard string index with dynamic mapping enabled.

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A wildcard index on the `name` field.

Question 16

A developer needs to quickly inspect the contents of a collection and run ad-hoc queries against a MongoDB Atlas cluster without installing any additional software. Which tool should be used?

Accepted Answer

Atlas Data Explorer. Atlas Data Explorer is a web-based interface integrated into MongoDB Atlas that lets users view and query data without installing any software. It is the most convenient tool for quick inspection and ad-hoc queries, as it is accessible directly from the Atlas UI and requires no local setup.

Answer

MongoDB Database Tools

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MongoDB Compass

Answer

mongosh

Question 17

A financial application stores transaction records in a MongoDB collection. Each transaction document includes a Decimal128 amount field and a Date field for the transaction time. A developer needs to query for transactions with amounts greater than 100.00 and times within the last 24 hours, and wants the queries to use indexes efficiently. Which statement about the document model and BSON types is correct for this scenario?

Accepted Answer

Date values are stored as 64-bit integers representing milliseconds since the Unix epoch, and they can be indexed and compared with range operators.. BSON Date is stored as a 64-bit integer of milliseconds since the Unix epoch, which makes it directly comparable and indexable for range queries. Decimal128 is a separate numeric type that also supports range comparisons and indexing. Together, they allow the application to filter by amount and time efficiently using appropriate indexes, without converting values to strings or avoiding range queries.

Answer

Range queries on Decimal128 fields are not supported; only equality queries can use an index on a Decimal128 field.

Answer

Decimal128 values are stored as strings, so range queries on the amount field require a text index rather than a numeric index.

Answer

Because Decimal128 and Date are both binary types, they cannot be used together in a compound index on the same collection.

Question 18

A developer is designing a MongoDB collection to store sensor readings. Each reading document contains a sensorId, a timestamp, and a readings array of subdocuments with type and value fields. The application frequently queries for readings where the readings array contains a subdocument with type "temperature" and value greater than 30. Which document model feature allows the query to match elements within the array efficiently?

Accepted Answer

Multikey indexes, which are created automatically when an index is built on a field that contains an array, allow the query to match individual array elements.. A multikey index is created when an index is built on a field containing an array. MongoDB generates an index entry for each array element, so queries that filter on subdocument fields within the array can use the index. This allows the sensor reading query to match temperature readings above 30 efficiently, as long as an appropriate index exists on the readings field.

Answer

The _id index, which is created on every collection, includes all array elements and can be used to match subdocument fields.

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Capped collections, which store documents in insertion order, automatically index array elements for fast lookups.

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Array flattening, which merges all subdocuments into a single document, allows the query to treat the readings array as a flat set of fields.

Question 19

A developer needs to load a large JSON file into a collection named 'inventory' in the 'retail' database on a MongoDB server running on localhost. The file contains one JSON document per line. Which command-line tool should be used?

Accepted Answer

mongoimport --db retail --collection inventory --file inventory.json. The correct tool is mongoimport, which is part of the MongoDB Database Tools and specifically built to import data from JSON, CSV, or TSV files into a collection. The other tools serve different purposes: mongorestore restores BSON dumps, mongoexport exports data, and mongofiles manages GridFS files. Only mongoimport matches the requirement of loading a JSON file into a collection.

Answer

mongofiles --db retail --collection inventory --file inventory.json

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mongoexport --db retail --collection inventory --file inventory.json

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mongorestore --db retail --collection inventory inventory.json

Question 20

You are the lead developer for a high-traffic e-commerce application using MongoDB 6.0. A query on the `orders` collection that filters on `customerId` and sorts by `orderDate` in descending order is performing poorly. You decide to create an index to improve its performance. Which of the following indexes would be most efficient for this query?

Accepted Answer

db.orders.createIndex({ customerId: 1, orderDate: -1 }). The most efficient index for a query with an equality filter and a sort is one that follows the Equality, Sort, Range (ESR) rule: equality fields first, then sort fields. The index must also match the sort direction. Here, the query filters on `customerId` (equality) and sorts by `orderDate` descending. Therefore, an index on `{ customerId: 1, orderDate: -1 }` allows MongoDB to use the index for both filtering and sorting, avoiding an expensive in-memory sort and ensuring optimal performance.

Answer

db.orders.createIndex({ customerId: 1, orderDate: 1 })

Answer

db.orders.createIndex({ orderDate: -1, customerId: 1 })

Answer

db.orders.createIndex({ orderDate: 1, customerId: 1 })