$avg accumulator in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Calculate average score per student

Given the following documents in the students collection:

{ "name": "Alice", "scores": [80, 90, 100] }
{ "name": "Bob", "scores": [70, 85, 90] }
{ "name": "Charlie", "scores": [60, 75, 85] }

Which aggregation pipeline will correctly calculate the average score for each student?

MongoDB

db.students.aggregate([
  { $project: { name: 1, avgScore: { $avg: "$scores" } } }
])

A [ { "$project": { "name": 1, "avgScore": { "$avg": "$scores" } } } ]

B [ { "$group": { "_id": "$name", "avgScore": { "$avg": "$scores" } } } ]

C [ { "$project": { "name": 1, "avgScore": { "$avg": "$scores.score" } } } ]

D [ { "$group": { "_id": "$name", "avgScore": { "$avg": "$scores.score" } } } ]

💡 Hint

Use $project to calculate average on an array field within each document.

The $avg accumulator can calculate the average of values in an array when used inside $project. Using $group here is incorrect because scores is an array inside each document, not a field to group by.

❓ query_result

intermediate

Average price per category

Consider a products collection with documents like:

{ "category": "Books", "price": 15 }
{ "category": "Books", "price": 25 }
{ "category": "Electronics", "price": 100 }

Which aggregation pipeline will return the average price for each category?

MongoDB

db.products.aggregate([
  { $group: { _id: "$category", avgPrice: { $avg: "$price" } } }
])

A [ { "$group": { "_id": "$category", "avgPrice": { "$avg": "$category.price" } } } ]

B [ { "$project": { "category": 1, "avgPrice": { "$avg": "$price" } } } ]

C [ { "$group": { "_id": "$category", "avgPrice": { "$avg": "$price" } } } ]

D [ { "$match": { "price": { "$exists": true } } }, { "$group": { "_id": "$category", "avgPrice": { "$avg": "$price" } } } ]

💡 Hint

Use $group to aggregate by category and calculate average price.

The $group stage groups documents by category and calculates the average price. $project cannot aggregate across documents. Option C adds an unnecessary $match but does not change correctness.

📝 Syntax

advanced

Identify the syntax error in $avg usage

Which of the following aggregation stages will cause a syntax error due to incorrect $avg usage?

A { $project: { average: { $avg: "$values" } } }

B { $group: { _id: "$type", average: { $avg: "$value" } } }

C { $group: { _id: "$type", average: { $avg: ["$value1", "$value2"] } } }

D { $group: { _id: "$type", average: { $avg: "$value", $sum: 1 } } }

💡 Hint

Check if the accumulator object contains invalid extra fields.

Option D incorrectly mixes $avg and $sum inside the same object, which is invalid syntax. Each accumulator must be a single operator.

❓ optimization

advanced

Optimize average calculation on large dataset

You want to calculate the average rating from a large reviews collection. Which pipeline is more efficient?

A [ { $match: { rating: { $exists: true } } }, { $group: { _id: null, avgRating: { $avg: "$rating" } } } ]

B [ { $group: { _id: null, avgRating: { $avg: "$rating" } } } ]

C [ { $project: { rating: 1 } }, { $group: { _id: null, avgRating: { $avg: "$rating" } } } ]

D [ { $group: { _id: "$userId", avgRating: { $avg: "$rating" } } } ]

💡 Hint

Filtering out documents without rating before grouping can improve performance.

Option A filters documents to only those with a rating before grouping, reducing data processed in $group. Option A processes all documents, including those without rating. Option A projects but does not filter. Option A groups by userId, not overall average.

🧠 Conceptual

expert

Understanding $avg behavior with missing or null values

What is the result of the following aggregation on this sales collection?

Documents:

{ "amount": 100 }
{ "amount": null }
{ }

Pipeline:

[ { $group: { _id: null, averageAmount: { $avg: "$amount" } } } ]

What will averageAmount be?

MongoDB

db.sales.aggregate([{ $group: { _id: null, averageAmount: { $avg: "$amount" } } }])

A null

B 100

C 50

D 0

💡 Hint

Consider how $avg treats null or missing fields in aggregation.

$avg ignores null and missing values when calculating the average. Only the value 100 is considered, so the average is 100.