Why the aggregation pipeline is needed in MongoDB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start an aggregation pipeline on the 'orders' collection.

MongoDB

db.orders.[1]([{ $match: { status: 'shipped' } }])

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

Ainsert

Bfind

Cupdate

Daggregate

Attempts:

3 left

💡 Hint

Common Mistakes

Using find() instead of aggregate() for complex data processing.

Trying to use insert() or update() which modify data, not process it.

The aggregate method starts an aggregation pipeline in MongoDB.

2fill in blank

medium

Complete the code to filter documents where 'age' is greater than 25 in the aggregation pipeline.

MongoDB

db.users.aggregate([{ $match: { age: { [1]: 25 } } }])

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

A$lt

B$eq

C$gt

D$ne

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt which means less than, opposite of what is needed.

Using $eq which means equal, not greater than.

The $gt operator means 'greater than' in MongoDB queries.

3fill in blank

hard

Fix the error in the aggregation stage that groups documents by 'category' and counts them.

MongoDB

db.products.aggregate([{ $group: { _id: '$category', count: { [1]: 1 } } }])

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

A$sum

B$avg

C$max

D$min

Attempts:

3 left

💡 Hint

Common Mistakes

Using $avg which calculates average, not count.

Using $max or $min which find max or min values, not counts.

The $sum operator counts the number of documents in each group.

4fill in blank

hard

Fill both blanks to project only the 'name' and 'price' fields in the aggregation pipeline.

MongoDB

db.items.aggregate([{ $project: { [1]: 1, [2]: 1, _id: 0 } }])

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

Aname

Bprice

Ccategory

Dquantity

Attempts:

3 left

💡 Hint

Common Mistakes

Including fields not needed like 'category' or 'quantity'.

Forgetting to exclude _id if not wanted.

Projecting 'name' and 'price' fields includes only these fields in the output.

5fill in blank

hard

Fill all three blanks to filter documents with 'score' greater than 80, then sort by 'score' descending, and limit to 5 results.

MongoDB

db.scores.aggregate([ { $match: { score: { [1]: 80 } } }, { $sort: { score: [2] } }, { $limit: [3] } ])

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

A$gt

B-1

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt instead of $gt for filtering.

Sorting ascending (1) instead of descending (-1).

Setting limit to a wrong number.

Use $gt to filter scores greater than 80, -1 to sort descending, and 5 to limit results to 5.

Practice

1. Why do we use the aggregation pipeline in MongoDB?

easy

A. To process and transform data step-by-step inside the database

B. To create new databases

C. To backup the database automatically

D. To connect MongoDB with other databases

Solution

1. Step 1: Understand the purpose of aggregation pipeline

   The aggregation pipeline is designed to process data in stages, allowing filtering, grouping, and transforming data inside MongoDB.

2. Step 2: Compare options with the pipeline's purpose

   Creating databases, backups, or connecting databases are not functions of the aggregation pipeline.

3. Final Answer:

   To process and transform data step-by-step inside the database -> Option A

4. Quick Check:

   Aggregation pipeline = step-by-step data processing [OK]

Hint: Aggregation pipeline processes data inside MongoDB stepwise [OK]

Common Mistakes:

• Confusing aggregation pipeline with database creation
• Thinking it handles backups
• Assuming it connects different databases

2. Which of the following is the correct syntax to start an aggregation pipeline on a collection named orders?

easy

A. db.orders.findAggregate([ { $match: { status: 'shipped' } } ])

B. db.orders.find({ $match: { status: 'shipped' } })

C. db.orders.aggregate({ $match: { status: 'shipped' } })

D. db.orders.aggregate([ { $match: { status: 'shipped' } } ])

Solution

1. Step 1: Identify the correct aggregation method

   The aggregation pipeline uses the aggregate() method with an array of stages.

2. Step 2: Check the syntax of each option

   db.orders.aggregate([ { $match: { status: 'shipped' } } ]) correctly uses aggregate with an array containing a $match stage. The other options use invalid methods like findAggregate(), misuse find() with aggregation stages, or pass an object instead of an array to aggregate().

3. Final Answer:

   db.orders.aggregate([ { $match: { status: 'shipped' } } ]) -> Option D

4. Quick Check:

   aggregate() takes an array of stages [OK]

Hint: aggregate() needs an array of stages, not an object [OK]

Common Mistakes:

• Using find() instead of aggregate() for pipelines
• Passing an object instead of an array to aggregate()
• Using non-existent methods like findAggregate()

3. Given the collection sales with documents:

{ "item": "apple", "qty": 5 }

{ "item": "banana", "qty": 10 }

{ "item": "apple", "qty": 7 }

What will this aggregation pipeline return?

db.sales.aggregate([ { $match: { item: "apple" } }, { $group: { _id: "$item", totalQty: { $sum: "$qty" } } } ])

medium

A. [{ "_id": "apple", "totalQty": 7 }]

B. [{ "_id": "banana", "totalQty": 10 }]

C. [{ "_id": "apple", "totalQty": 12 }]

D. []

Solution

1. Step 1: Filter documents with $match

   The pipeline first selects documents where item is "apple". These are the first and third documents with quantities 5 and 7.

2. Step 2: Group and sum quantities with $group

   The pipeline groups by item and sums the qty values: 5 + 7 = 12.

3. Final Answer:

   [{ "_id": "apple", "totalQty": 12 }] -> Option C

4. Quick Check:

   Sum qty for apples = 12 [OK]

Hint: Match filters first, then group sums values [OK]

Common Mistakes:

• Summing all items instead of filtered ones
• Confusing _id field in group stage
• Ignoring the $match filter

4. What is wrong with this aggregation pipeline?

db.products.aggregate({ $sort: { price: 1 } })

medium

A. The field name price is invalid

B. The $sort stage must be inside an array

C. aggregate() cannot sort data

D. Missing a $match stage before $sort

Solution

1. Step 1: Check aggregate() method syntax

   The aggregate() method requires an array of stages, even if there is only one stage.

2. Step 2: Identify the error in the pipeline

   The pipeline is passed as an object instead of an array, causing a syntax error.

3. Final Answer:

   The $sort stage must be inside an array -> Option B

4. Quick Check:

   aggregate() needs array of stages [OK]

Hint: Always wrap pipeline stages in an array [OK]

Common Mistakes:

• Passing pipeline as object instead of array
• Assuming aggregate() can't sort
• Thinking $match is required before $sort

5. You want to find the average quantity sold per item from a transactions collection. Which aggregation pipeline correctly achieves this?

hard

A. db.transactions.aggregate([ { $group: { _id: "$item", avgQty: { $avg: "$qty" } } } ])

B. db.transactions.aggregate([ { $match: { qty: { $avg: true } } } ])

C. db.transactions.aggregate([ { $sort: { qty: 1 } }, { $avg: "$qty" } ])

D. db.transactions.aggregate([ { $group: { _id: "$qty", avgQty: { $sum: "$item" } } } ])

Solution

1. Step 1: Understand the goal

   We want to group documents by item and calculate the average of qty for each group.

2. Step 2: Analyze each option

   db.transactions.aggregate([ { $group: { _id: "$item", avgQty: { $avg: "$qty" } } } ]) groups by item and uses $avg on qty, which is correct. db.transactions.aggregate([ { $match: { qty: { $avg: true } } } ]) misuses $avg inside $match. db.transactions.aggregate([ { $sort: { qty: 1 } }, { $avg: "$qty" } ]) incorrectly uses $avg as a pipeline stage. db.transactions.aggregate([ { $group: { _id: "$qty", avgQty: { $sum: "$item" } } } ]) groups by qty and sums item, which is wrong.

3. Final Answer:

   db.transactions.aggregate([ { $group: { _id: "$item", avgQty: { $avg: "$qty" } } } ]) -> Option A

4. Quick Check:

   Group by item and average qty = db.transactions.aggregate([ { $group: { _id: "$item", avgQty: { $avg: "$qty" } } } ]) [OK]

Hint: Use $group with $avg to calculate averages per group [OK]

Common Mistakes:

• Using $avg inside $match stage
• Trying to use $avg as a pipeline stage
• Grouping by wrong field or summing instead of averaging