C100DEV Practice Test 20 — 15 Questions

Question 1

Which operator is used within a $project stage to calculate the square root of a numerical field?

Accepted Answer

$sqrt. The $sqrt operator is part of the aggregation framework's mathematical expression set. It is specifically designed to compute the square root of a given number or expression. Understanding available mathematical operators allows developers to perform complex calculations directly within the database, minimizing the need to retrieve raw data and process it in the application layer, which improves overall system efficiency.

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$pow

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$abs

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$exp

Question 2

What is the purpose of the $accumulator stage in the aggregation framework?

Accepted Answer

To define custom aggregation logic.. The $accumulator stage allows developers to define custom aggregation logic using JavaScript functions. This is useful for scenarios where built-in accumulators like $sum or $avg are insufficient, such as calculating complex statistical models or performing custom data parsing that requires state maintenance during the grouping process. Because it executes JavaScript, it should be used judiciously, as it can be slower than native pipeline operators.

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To replace $group.

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To improve performance over native operators.

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To perform joins across collections.

Question 3

Which operator is most appropriate to concatenate an array of strings into a single string field?

Accepted Answer

$reduce. The $reduce operator provides a way to iterate through an array and apply an expression to each element, allowing you to build a cumulative result. When used with $concat, it is the standard and most performant way to join array elements into a single string. This is common when normalizing document data for export or display purposes in external applications.

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$map

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$concat

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$filter

Question 4

When designing a schema for a time-series dataset, why is it recommended to use the 'Bucket' pattern?

Accepted Answer

It reduces index size and improves read performance by grouping data.. The Bucket pattern groups multiple related documents (like sensor readings) into a single document representing a time window (e.g., one hour). This significantly reduces the total number of documents in the collection, which improves index efficiency and reduces the memory footprint. By minimizing the index size, the database can keep more of the index in RAM, leading to faster query performance for analytical or time-based data retrieval.

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It allows for unlimited document growth without checking the 16MB limit.

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It automatically converts all data to a binary format for faster storage.

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It enables ACID transactions for non-related collections automatically.

Question 5

What is the primary purpose of the 'causal consistency' feature provided by MongoDB sessions?

Accepted Answer

To ensure that a client reads the results of its own writes.. Causal consistency ensures that operations within a session appear in an order that respects the causal relationship between them, even when reading from secondary nodes. This is vital in distributed systems to prevent 'time-travel' anomalies, such as reading a stale version of a document immediately after an update. By using the operation time, MongoDB guarantees that a user sees their own writes immediately, maintaining a logical progression of data state.

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To force all reads and writes to occur on the primary node only.

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To improve overall write throughput by batching operations.

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To automatically shard data based on access patterns.

Question 6

In an aggregation pipeline, what is the purpose of the $addFields stage?

Accepted Answer

It adds new fields to documents, preserving existing fields.. The $addFields stage allows you to append new fields to documents by defining expressions. It is highly versatile, enabling computed fields, data restructuring, or field renaming without removing existing data. This is crucial for enriching data on the fly within the database, which avoids the overhead of modifying large collections on disk or performing complex logic within the application code.

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It removes all existing fields and replaces them with new ones.

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It filters the collection based on newly added fields.

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It is a deprecated stage replaced by $project.

Question 7

You need to ensure that values in the 'email' field are unique across all documents in a collection, but documents where the 'email' field is missing should not violate this constraint. How should you create this index?

Accepted Answer

Create a unique partial index with a filter expression requiring email to exist. Partial indexes allow you to restrict the indexing operation to a subset of documents that meet a specified filter expression. By combining a unique constraint with a partial filter expression requiring the field to exist, you prevent duplicate values for present fields while allowing multiple documents to omit the field entirely.

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Create a standard unique index on the email field

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Create a sparse index on the email field without uniqueness

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Create a wildcard index on the email field

Question 8

Which property of a Multikey Index is true when indexing an array field?

Accepted Answer

It creates an index entry for each element in the array.. A Multikey Index is automatically created when an index is placed on a field containing an array. The index creates an entry for each element in the array, effectively mapping the index key to the parent document. This allows queries to efficiently find documents that contain specific values within their arrays. However, it also means that a single document can have multiple entries in the index, which is a key technical distinction.

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A document can only have one index entry in a multikey index.

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Multikey indexes cannot index array fields.

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Multikey indexes are created manually by the user.

Question 9

Your application executes the query: db.users.find({ age: { $gt: 30 } }, { age: 1, _id: 0 }). You have an index on { age: 1 }. How does MongoDB process this query?

Accepted Answer

This is a covered query, and MongoDB retrieves all data from the index alone.. A covered query occurs when the index contains all the fields required by both the query filter and the projection. Because the query only requests the 'age' field and explicitly excludes '_id', MongoDB can fulfill the entire request by looking only at the index. This avoids the need to fetch documents from the heap, significantly improving performance.

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MongoDB performs an index scan followed by a fetch to get the 'age' values.

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MongoDB must fetch the documents because projections are not supported in covered queries.

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The query cannot be covered because the _id field is always required from the document.

Question 10

Which TWO of the following are benefits of using the Polymorphic Pattern in MongoDB?

Accepted Answer

It allows a single query to retrieve all related items across different types.. The polymorphic pattern allows you to store different document structures in the same collection, which is useful when entities share common fields but have unique characteristics. By keeping these in one collection, you simplify queries that need to target the entire entity set and reduce the need for complex application-level logic to manage multiple collections for similar object types, improving developer productivity and consistency.

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It enforces strict schema validation for all documents within the collection.

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It automatically increases the write throughput of the database cluster.

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It allows for unlimited document size by splitting fields across collections.

Question 11

Refer to the exhibit. Which MongoDB design pattern is represented here?

Accepted Answer

The Attribute pattern.. This is the Attribute pattern. It is used when you need to store documents where fields vary significantly or when you need to index multiple fields that share a common structure. By transforming key-value pairs into an array of sub-documents, you can create a single multikey index on the 'specs.k' and 'specs.v' fields, allowing for efficient queries across diverse attribute sets that would otherwise require too many individual indexes.

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The Bucket pattern.

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The Extended Reference pattern.

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The Computed pattern.

Question 12

What is the purpose of the $merge stage in an aggregation pipeline?

Accepted Answer

To write results into a collection, optionally updating existing documents.. The $merge stage is used to output the aggregation result to a collection, allowing for upserts and merging data into an existing collection. Unlike $out, which replaces the entire collection, $merge provides greater flexibility for incremental updates. This makes it ideal for maintaining materialized views or updating existing summary tables with new data without the performance cost of rewriting the entire target collection from scratch.

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To filter the pipeline based on multiple conditions.

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To replace the entire contents of a collection with new results.

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To sort the final output before it is returned to the client.

Question 13

You are designing a schema for a social media platform. A user has a 'profile' document, and they can have thousands of 'followers'. How should you model the follower relationship?

Accepted Answer

Create a separate collection for followers and store references.. Storing thousands of followers in an array within the user profile document is a violation of the unbounded array anti-pattern, as it would cause the document to grow beyond the 16MB limit and result in poor write performance. Referencing the followers in a separate collection, where each follower is a separate document, provides a scalable solution that supports high growth and efficient querying without performance degradation.

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Embed all follower IDs in an array within the user profile document.

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Use the Attribute Pattern to store each follower as a key-value pair.

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Store the followers in a gridFS file to handle the large size.

Question 14

A developer wants to implement full-text search on a collection of articles. They create a text index on the 'content' field. Which of the following is a significant limitation of 'text' indexes in MongoDB?

Accepted Answer

A collection can have at most one 'text' index.. A collection in MongoDB can have at most one text index. However, this single index can cover multiple fields by creating a compound text index. This limitation means developers must carefully plan which fields need to be searchable via text commands and consolidate them into a single index definition for that collection.

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Text indexes do not support case-insensitive searches.

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Text indexes cannot be used in a sharded cluster.

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You cannot use 'text' indexes with the $match operator in aggregation.

Question 15

Which of the following describes the behavior of a 'write concern' in a MongoDB transaction?

Accepted Answer

It is specified during the commitTransaction command.. Transactions in MongoDB honor the write concern configured on the 'commitTransaction' command. This allows developers to specify the durability level, such as 'majority', ensuring that changes are acknowledged by the majority of replica set members before the transaction is considered complete. Proper management of write concerns ensures that even if a primary node fails immediately after a commit, the data remains durable and consistent across the cluster.

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Write concern is ignored inside multi-document transactions.

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It is applied to each individual operation within the transaction.

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It is determined solely by the server's default configuration.