One-to-one embedding pattern in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Find the embedded document in a one-to-one pattern

Given a collection users where each user document embeds a single profile document, what will be the output of the following query?

{ _id: 1, name: "Alice", profile: { age: 30, city: "NY" } }

Query:

db.users.findOne({ _id: 1 }, { "profile.city": 1, _id: 0 })

MongoDB

db.users.findOne({ _id: 1 }, { "profile.city": 1, _id: 0 })

A {"profile.city": "NY"}

B {"profile": {"city": "NY"}}

C {"city": "NY"}

D {"profile": {"age": 30, "city": "NY"}}

💡 Hint

Projection returns the embedded document fields as nested objects.

The projection { "profile.city": 1, _id: 0 } returns the profile field with only the city subfield included. The result nests city inside profile.

📝 Syntax

intermediate

Identify the correct syntax for embedding one-to-one documents

Which of the following MongoDB insert statements correctly embeds a one-to-one address document inside a customer document?

A db.customers.insertOne({ _id: 101, name: "Bob" }, { address: { street: "Main St", number: 123 } })

B db.customers.insertOne({ _id: 101, name: "Bob" }, { $set: { address: { street: "Main St", number: 123 } } })

C db.customers.insertOne({ _id: 101, name: "Bob", address: [ { street: "Main St", number: 123 } ] })

D db.customers.insertOne({ _id: 101, name: "Bob", address: { street: "Main St", number: 123 } })

💡 Hint

Embedding means nesting the document inside the main document in one insert.

Option D correctly nests the address document inside the customer document in a single insert. Options B and D use incorrect syntax for insertOne. Option D uses an array which is not one-to-one embedding.

❓ optimization

advanced

Optimize query performance for one-to-one embedded documents

You have a products collection where each product embeds a specs document. You want to frequently query products by a field inside specs, for example specs.weight. What is the best way to optimize these queries?

A Create an index on <code>specs.weight</code> field

B Create a text index on the entire <code>specs</code> document

C Split <code>specs</code> into a separate collection and use references

D Use aggregation pipeline to filter <code>specs.weight</code> after fetching all products

💡 Hint

Indexes speed up queries on specific fields.

Creating an index on specs.weight allows MongoDB to quickly find products by that field inside the embedded document. Text indexes are for string search, splitting collections adds complexity, and filtering after fetching all data is inefficient.

🔧 Debug

advanced

Debug why embedded document is missing in query result

You have documents with embedded profile in users. You run this query:

db.users.find({}, { name: 1 })

But the profile field is missing in the results. Why?

MongoDB

db.users.find({}, { name: 1 })

A Because projection only includes <code>name</code> and excludes all other fields including <code>profile</code>

B Because the query syntax is invalid and returns no fields

C Because <code>profile</code> field is empty in all documents

D Because <code>profile</code> is not embedded but referenced

💡 Hint

Projection controls which fields appear in results.

When projection specifies only name: 1, MongoDB returns only the name field and excludes all others by default, including profile. To include profile, it must be explicitly projected.

🧠 Conceptual

expert

Why choose one-to-one embedding over referencing?

In MongoDB, when designing a schema for a one-to-one relationship, why might embedding the related document be better than using references?

A Embedding enforces stronger data consistency by using foreign key constraints

B Embedding allows unlimited growth of the embedded document without size limits

C Embedding reduces the need for joins and improves read performance by storing related data together

D Embedding automatically creates indexes on all embedded fields

💡 Hint

Think about how data is stored and accessed in MongoDB.

Embedding stores related data in the same document, so queries can retrieve all data in one read without joins. MongoDB documents have size limits, no foreign key constraints, and indexes must be created explicitly.