Why advanced stages matter in MongoDB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find all documents in the collection.

MongoDB

db.collection.[1]()

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

Afind

Bupdate

Cinsert

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'insert' instead of 'find' will add data, not retrieve it.

Using 'update' or 'delete' changes or removes data, not fetches it.

The find() method retrieves all documents from a MongoDB collection.

2fill in blank

medium

Complete the code to filter documents where age is greater than 25.

MongoDB

db.collection.find({ age: { [1]: 25 } })

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

A$gt

B$lt

C$eq

D$ne

Attempts:

3 left

💡 Hint

Common Mistakes

Using '$lt' will find ages less than 25, not greater.

Using '$eq' looks for exactly 25, not greater.

Using '$ne' finds ages not equal to 25.

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

3fill in blank

hard

Fix the error in the aggregation pipeline to group by 'category'.

MongoDB

db.collection.aggregate([{ $group: { _id: "$ [1]" } }])

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

ACategory

Bcategory

Ccat

Dcategories

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'categories' or 'Category' will not match the field.

Using 'cat' is not the correct field name.

The field name must exactly match the document field, which is 'category'.

4fill in blank

hard

Fill both blanks to project only the 'name' and 'score' fields in the output.

MongoDB

db.collection.find({}, { [1]: 1, [2]: 1 })

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

Aname

Bscore

Cage

Daddress

Attempts:

3 left

💡 Hint

Common Mistakes

Including fields like 'age' or 'address' will show unwanted data.

Leaving out fields will exclude them from the output.

Projection specifies which fields to include. Here, 'name' and 'score' are included.

5fill in blank

hard

Fill all three blanks to update the 'status' field to 'active' for documents where 'age' is less than 30.

MongoDB

db.collection.updateMany({ age: { [1]: 30 } }, { [2]: { [3]: "active" } })

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

A$lt

B$set

Cstatus

D$gt

Attempts:

3 left

💡 Hint

Common Mistakes

Using '$gt' will select ages greater than 30, which is wrong.

Using wrong update operators will cause errors or no changes.

Misspelling the field name 'status' will fail the update.

The query uses $lt to find ages less than 30, then $set to update the 'status' field to 'active'.

Practice

1. What is the main reason to use advanced stages in MongoDB aggregation pipelines?

easy

A. To process data step-by-step for clearer results

B. To store data in multiple collections

C. To create indexes automatically

D. To backup the database

Solution

1. Step 1: Understand the purpose of advanced stages

   Advanced stages in MongoDB aggregation allow processing data in steps like filtering, grouping, and sorting.

2. Step 2: Identify the benefit of step-by-step processing

   This stepwise processing helps produce clear and useful results from complex data.

3. Final Answer:

   To process data step-by-step for clearer results -> Option A

4. Quick Check:

   Advanced stages = step-by-step processing [OK]

Hint: Think of stages as steps to clean and organize data [OK]

Common Mistakes:

• Confusing aggregation stages with indexing
• Thinking advanced stages create backups
• Believing stages store data in collections

2. Which of the following is the correct syntax to start an aggregation pipeline in MongoDB?

easy

A. db.collection.match([{ age: { $gt: 25 } }])

B. db.collection.find({ $match: { age: { $gt: 25 } } })

C. db.collection.aggregate([{ $match: { age: { $gt: 25 } } }])

D. db.collection.aggregate({ $match: { age: { $gt: 25 } } })

Solution

1. Step 1: Recall the aggregation method syntax

   The aggregation pipeline starts with db.collection.aggregate() and takes an array of stages.

2. Step 2: Check the correct use of $match stage inside an array

   The $match stage must be inside an array as the first element to filter documents.

3. Final Answer:

   db.collection.aggregate([{ $match: { age: { $gt: 25 } } }]) -> Option C

4. Quick Check:

   aggregate() takes an array of stages [OK]

Hint: Remember aggregate() needs an array of stages [OK]

Common Mistakes:

• Using find() instead of aggregate() for pipeline
• Passing object instead of array to aggregate()
• Using non-existent match() method

3. Given the collection 'sales' with documents { item: 'apple', qty: 5 } and { item: 'banana', qty: 10 }, what is the output of this pipeline?

db.sales.aggregate([
  { $match: { qty: { $gt: 5 } } },
  { $project: { item: 1, _id: 0 } }
])

medium

A. [{ item: 'apple' }]

B. [{ item: 'banana' }]

C. [{ item: 'apple' }, { item: 'banana' }]

D. []

Solution

1. Step 1: Apply the $match stage filtering qty > 5

   Only the document with qty 10 (banana) matches the condition.

2. Step 2: Apply the $project stage to include item and exclude _id

   The output will show only the item field for the matched document.

3. Final Answer:

   [{ item: 'banana' }] -> Option B

4. Quick Check:

   Filter qty > 5 then project item only [OK]

Hint: Filter first, then project fields you want [OK]

Common Mistakes:

• Including documents with qty <= 5
• Forgetting to exclude _id field
• Projecting fields before filtering

4. Identify the error in this aggregation pipeline:

db.orders.aggregate([
  { $group: { _id: '$customerId', total: { $sum: '$amount' } } },
  { $sort: { total: -1 } },
  { $match: { total: { $gt: 100 } } }
])

medium

A. The $sort stage cannot be used after $group

B. The $match stage should come before $group

C. The $match stage cannot filter on computed fields after $group

D. No error, pipeline is correct

Solution

1. Step 1: Analyze the $group stage

   It correctly groups by customerId and computes the total sum per customer.

2. Step 2: Verify $sort and $match stages

   $sort by total descending after $group is valid. $match filtering groups with total > 100 after $group is also valid on computed fields.

3. Final Answer:

   No error, pipeline is correct -> Option D

4. Quick Check:

   $match after $group on aggregates is allowed [OK]

Hint: $match can filter aggregates after $group [OK]

Common Mistakes:

• Thinking $match must always precede $group
• Believing $sort cannot follow $group
• Assuming $match cannot filter computed fields

5. You want to find the top 3 products by total sales amount from a 'sales' collection. Which pipeline correctly uses advanced stages to achieve this?

hard

A. [{ $group: { _id: '$product', totalSales: { $sum: '$amount' } } }, { $sort: { totalSales: -1 } }, { $limit: 3 }]

B. [{ $sort: { amount: -1 } }, { $limit: 3 }, { $group: { _id: '$product', totalSales: { $sum: '$amount' } } }]

C. [{ $limit: 3 }, { $group: { _id: '$product', totalSales: { $sum: '$amount' } } }, { $sort: { totalSales: -1 } }]

D. [{ $group: { _id: '$product', totalSales: { $sum: '$amount' } } }, { $limit: 3 }, { $sort: { totalSales: -1 } }]

Solution

1. Step 1: Group sales by product and sum amounts

   The $group stage aggregates total sales per product correctly.

2. Step 2: Sort products by totalSales descending and limit to top 3

   $sort orders by totalSales descending, then $limit picks top 3 products.

3. Final Answer:

   [{ $group: { _id: '$product', totalSales: { $sum: '$amount' } } }, { $sort: { totalSales: -1 } }, { $limit: 3 }] -> Option A

4. Quick Check:

   Group, then sort, then limit for top results [OK]

Hint: Group first, then sort and limit for top results [OK]

Common Mistakes:

• Limiting before grouping loses data
• Sorting before grouping gives wrong totals
• Placing $limit before $sort causes wrong output