Why expressions matter in pipelines in MongoDB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new field 'total' that sums 'price' and 'tax' in each document.

MongoDB

db.sales.aggregate([{ $addFields: { total: { $[1]: ["$price", "$tax"] } } }])

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

Aadd

Bsum

Cmultiply

Dconcat

Attempts:

3 left

💡 Hint

Common Mistakes

Using $sum instead of $add inside $addFields

Using $multiply which multiplies values

Using $concat which joins strings

The $add expression adds numbers in MongoDB pipelines. Here, it sums 'price' and 'tax'.

2fill in blank

medium

Complete the code to filter documents where 'score' is greater than 80 using $match and $expr.

MongoDB

db.students.aggregate([{ $match: { $expr: { $[1]: ["$score", 80] } } }])

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

Aeq

Bgt

Clt

Dne

Attempts:

3 left

💡 Hint

Common Mistakes

Using $eq which checks equality

Using $lt which checks less than

Using $ne which checks not equal

The $gt expression checks if the first value is greater than the second.

3fill in blank

hard

Fix the error in the pipeline to correctly compute the average of 'marks' array using $avg.

MongoDB

db.exams.aggregate([{ $project: { average: { $[1]: "$marks" } } }])

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

Asum

Bmin

Cmax

Davg

Attempts:

3 left

💡 Hint

Common Mistakes

Using $sum which adds values

Using $max or $min which find extremes

The $avg expression calculates the average of values in an array.

4fill in blank

hard

Fill both blanks to create a pipeline that filters documents where 'age' is less than 30 and then projects only 'name' and 'age'.

MongoDB

db.people.aggregate([{ $match: { $expr: { $[1]: ["$age", 30] } } }, { $project: { [2]: 1, age: 1 } }])

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

Alt

Bgt

Cname

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using $gt instead of $lt

Projecting wrong fields like 'score'

$lt filters ages less than 30. Projecting 'name' and 'age' shows only these fields.

5fill in blank

hard

Fill all three blanks to create a pipeline that adds a field 'discountedPrice' by subtracting 'discount' from 'price', then filters where 'discountedPrice' is greater than 100.

MongoDB

db.products.aggregate([{ $addFields: { discountedPrice: { $[1]: ["$price", "$discount"] } } }, { $match: { $expr: { $[2]: ["$discountedPrice", [3]] } } }])

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

Asubtract

Bgt

C100

Dadd

Attempts:

3 left

💡 Hint

Common Mistakes

Using $add instead of $subtract

Using $lt instead of $gt

Filtering with wrong value

$subtract calculates discounted price. $gt filters prices greater than 100.

Practice

1. What is the main reason expressions are important in MongoDB aggregation pipelines?

easy

A. They backup the database during aggregation.

B. They create new collections automatically.

C. They replace the need for indexes.

D. They allow you to calculate and transform data within pipeline stages.

Solution

1. Step 1: Understand the role of expressions in pipelines

   Expressions let you perform calculations and data transformations inside stages like $project and $group.

2. Step 2: Compare other options

   Options A, B, and C describe unrelated features not handled by expressions.

3. Final Answer:

   They allow you to calculate and transform data within pipeline stages. -> Option D

4. Quick Check:

   Expressions = Data transformation [OK]

Hint: Expressions transform data inside stages like $project [OK]

Common Mistakes:

• Thinking expressions create collections
• Confusing expressions with indexing
• Assuming expressions handle backups

2. Which of the following is the correct syntax to use an expression in a $project stage to add two fields price and tax?

easy

A. { $project: { total: { $add: ["$price", "$tax"] } } }

B. { $project: { total: $add(price, tax) } }

C. { $project: { total: { add: [price, tax] } } }

D. { $project: { total: { $sum: "$price" + "$tax" } } }

Solution

1. Step 1: Identify correct expression syntax

   The $add operator requires an array of field references with $ prefix inside an object.

2. Step 2: Check each option

   { $project: { total: { $add: ["$price", "$tax"] } } } correctly uses $add with an array of "$price" and "$tax". Others misuse syntax or operators.

3. Final Answer:

   { $project: { total: { $add: ["$price", "$tax"] } } } -> Option A

4. Quick Check:

   $add needs array with $field names [OK]

Hint: Use $add with array of "$field" names in $project [OK]

Common Mistakes:

• Omitting $ before field names
• Using parentheses instead of array for $add
• Confusing $add with $sum operator

3. Given the pipeline stage:

{ $project: { discountedPrice: { $multiply: ["$price", 0.9] } } }

What will be the output for a document { price: 100 }?

medium

A. { discountedPrice: 10 }

B. { discountedPrice: 90 }

C. { discountedPrice: 100 }

D. { discountedPrice: 0.9 }

Solution

1. Step 1: Understand $multiply expression

   $multiply multiplies the values in the array. Here, price (100) * 0.9 = 90.

2. Step 2: Apply to given document

   Input price is 100, so discountedPrice = 100 * 0.9 = 90.

3. Final Answer:

   { discountedPrice: 90 } -> Option B

4. Quick Check:

   100 * 0.9 = 90 [OK]

Hint: Multiply fields with constants using $multiply [OK]

Common Mistakes:

• Confusing multiplication with subtraction
• Using 0.9 as a field instead of a number
• Expecting original price unchanged

4. Identify the error in this pipeline stage:

{ $project: { total: { $add: "$price", "$tax" } } }

medium

A. The $project stage cannot use $add operator.

B. Field names should not have $ prefix inside $add.

C. The $add operator requires an array of expressions, not separate arguments.

D. The syntax is correct; no error.

Solution

1. Step 1: Review $add operator syntax

   $add expects a single array argument containing expressions, not multiple separate arguments.

2. Step 2: Analyze given code

   The code passes "$price" and "$tax" as separate arguments instead of inside an array.

3. Final Answer:

   The $add operator requires an array of expressions, not separate arguments. -> Option C

4. Quick Check:

   $add needs array input [OK]

Hint: Always pass array to $add, not separate arguments [OK]

Common Mistakes:

• Passing multiple arguments instead of array
• Removing $ from field names inside expressions
• Assuming $project can't use expressions

5. You want to create a pipeline that groups sales by product and calculates the average revenue per sale, but only for sales where quantity is greater than 5. Which expression usage is correct inside the pipeline?

hard

A. { $match: { quantity: { $gt: 5 } } }, { $group: { _id: "$product", avgRevenue: { $avg: { $multiply: ["$price", "$quantity"] } } } }

B. { $group: { _id: "$product", avgRevenue: { $avg: { $multiply: ["$price", "$quantity"] } } } }, { $match: { quantity: { $gt: 5 } } }

C. { $match: { quantity: { $lt: 5 } } }, { $group: { _id: "$product", avgRevenue: { $avg: "$price" } } }

D. { $group: { _id: "$product", avgRevenue: { $sum: "$price" } } }, { $match: { quantity: { $gt: 5 } } }

Solution

1. Step 1: Filter sales with $match before grouping

   To include only sales with quantity > 5, $match must come before $group.

2. Step 2: Use $avg with $multiply expression inside $group

   Calculate revenue per sale as price * quantity, then average it per product.

3. Step 3: Check options

   { $match: { quantity: { $gt: 5 } } }, { $group: { _id: "$product", avgRevenue: { $avg: { $multiply: ["$price", "$quantity"] } } } } correctly orders stages and uses expressions. Others have wrong order or wrong operators.

4. Final Answer:

   { $match: { quantity: { $gt: 5 } } }, { $group: { _id: "$product", avgRevenue: { $avg: { $multiply: ["$price", "$quantity"] } } } } -> Option A

5. Quick Check:

   Filter then group with $avg of $multiply [OK]

Hint: Filter first with $match, then use expressions in $group [OK]

Common Mistakes:

• Placing $match after $group
• Using $sum instead of $avg for average
• Filtering with wrong comparison operator