How MongoDB stores data as documents - Practice Exercises

Challenge - 5 Problems

❓ query_result

intermediate

What does this MongoDB document look like after insertion?

Consider the following MongoDB insert operation:

db.users.insertOne({ name: "Alice", age: 30, hobbies: ["reading", "hiking"] })

What will the stored document look like when retrieved?

MongoDB

db.users.findOne({ name: "Alice" })

A { "name": "Alice", "age": "30", "hobbies": "reading, hiking" }

B { "_id": ObjectId(...), "name": "Alice", "age": 30, "hobbies": ["reading", "hiking"] }

C { "_id": ObjectId(...), "name": "Alice", "age": "30", "hobbies": ["reading", "hiking"] }

D { "name": "Alice", "age": 30, "hobbies": ["reading", "hiking"] }

💡 Hint

Remember MongoDB automatically adds an _id field and preserves data types.

MongoDB stores documents with an automatic _id field of type ObjectId unless specified. The age remains a number, and hobbies is stored as an array.

🧠 Conceptual

intermediate

How does MongoDB store nested data?

Which of the following best describes how MongoDB stores nested data inside documents?

A As embedded documents within the main document using key-value pairs.

B As flat JSON strings inside a single field.

C As separate tables linked by foreign keys.

D As separate files on disk referenced by the document.

💡 Hint

Think about how JSON objects can contain other objects.

MongoDB stores nested data as embedded documents inside the main document, allowing hierarchical data representation.

📝 Syntax

advanced

Which MongoDB query correctly updates a nested field?

Given a document structure:

{ name: "Bob", address: { city: "NY", zip: 10001 } }

Which query correctly updates the city to "LA"?

A db.collection.updateOne({ name: "Bob" }, { $set: { address: { city: "LA" } } })

B db.collection.updateOne({ name: "Bob" }, { city: "LA" })

C db.collection.updateOne({ name: "Bob" }, { $set: { "address.city": "LA" } })

D db.collection.updateOne({ name: "Bob" }, { $update: { "address.city": "LA" } })

💡 Hint

Use dot notation with $set to update nested fields.

Option C uses the correct $set operator with dot notation to update the nested city field without overwriting the entire address.

❓ optimization

advanced

How to optimize queries on nested document fields?

You have a collection with documents containing nested fields like:

{ user: { name: "Eve", age: 25 }, status: "active" }

Which approach best improves query performance filtering by user name?

A Use a text index on the entire document.

B Create an index on the status field only.

C Flatten the document to remove nesting.

D Create an index on the nested field user.name.

💡 Hint

Indexes on fields used in queries speed up searches.

Creating an index on user.name allows MongoDB to quickly find documents by that nested field without scanning all documents.

🔧 Debug

expert

Why does this MongoDB query return no results?

Given documents:

{ name: "Sam", details: { age: 40, city: "Boston" } }

Which query will NOT return this document?

Options:
A) db.collection.find({ "details.age": 40 })
B) db.collection.find({ details: { age: 40, city: "Boston" } })
C) db.collection.find({ details: { city: "Boston", age: 40 } })
D) db.collection.find({ "details.city": "Boston" })

A Returns no documents because field order in embedded document matters.

B Returns the document because it matches the nested city field.

C Returns the document because exact object match requires field order.

D Returns the document because it matches the nested age field.

💡 Hint

Exact matches on embedded documents require the same field order.

MongoDB matches embedded documents exactly including field order. Option A has different order and returns no results. Option A matches the document and returns the document. Therefore, option A is correct as NOT returning results.