Schema design for read-heavy workloads in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a collection optimized for fast reads by embedding related data.

MongoDB

db.users.insertOne({ name: "Alice", orders: [1] })

Drag options to blanks, or click blank then click option'

Anull

B[{ item: "book", qty: 2 }, { item: "pen", qty: 5 }]

CObjectId("507f1f77bcf86cd799439011")

D"orders"

Attempts:

3 left

💡 Hint

Common Mistakes

Referencing orders by ID instead of embedding causes slower reads.

Using null or unrelated strings does not store order data.

Embedding the orders array inside the user document allows faster reads by avoiding joins.

2fill in blank

medium

Complete the query to find users with embedded orders containing item 'book'.

MongoDB

db.users.find({ "orders.item": [1] })

Drag options to blanks, or click blank then click option'

A"notebook"

B"pen"

C"book"

D"pencil"

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong item name returns no results.

Querying on the wrong field does not find embedded documents.

Querying with "orders.item": "book" finds users whose embedded orders include a book.

3fill in blank

hard

Fix the error in the update to add a new embedded order to user 'Alice'.

MongoDB

db.users.updateOne({ name: "Alice" }, { [1]: { orders: { item: "notebook", qty: 3 } } })

Drag options to blanks, or click blank then click option'

A$push

B$set

C$addToSet

D$inc

Attempts:

3 left

💡 Hint

Common Mistakes

Using $set replaces the entire orders array instead of adding.

Using $inc is for numbers, not arrays.

$push adds a new element to the embedded orders array, which is needed to add an order.

4fill in blank

hard

Fill both blanks to create an index on the embedded field for faster reads.

MongoDB

db.users.createIndex({ [1]: [2] })

Drag options to blanks, or click blank then click option'

Aorders.item

B1

C-1

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Indexing the wrong field does not improve query speed.

Using -1 creates descending index, which is less common here.

Creating an ascending index on 'orders.item' speeds up queries filtering by that embedded field.

5fill in blank

hard

Fill all three blanks to write an aggregation pipeline that unwinds orders and filters by qty > 2.

MongoDB

db.users.aggregate([ { $unwind: "$[1]" }, { $match: { "[2].qty": { [3]: 2 } } } ])

Drag options to blanks, or click blank then click option'

Aorders

C$gt

D$lt

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt filters for less than, which is incorrect here.

Not unwinding the array prevents filtering individual orders.

$unwind deconstructs the orders array, then $match filters orders with qty greater than 2 using $gt.