Aggregation for reporting dashboards in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Calculate total sales per product category

Given a MongoDB collection sales with documents containing category and amount fields, which aggregation pipeline produces the total sales amount for each category?

MongoDB

db.sales.aggregate([
  { $group: { _id: "$category", totalSales: { $sum: "$amount" } } }
])

A [{ $group: { _id: "$category", totalSales: { $max: "$amount" } } }]

B [{ $match: { amount: { $gt: 0 } } }, { $group: { _id: "$category", totalSales: { $avg: "$amount" } } }]

C [{ $group: { _id: "$category", totalSales: { $sum: "$amount" } } }]

D [{ $project: { category: 1, totalSales: { $sum: "$amount" } } }]

💡 Hint

Use $group to aggregate data by category and $sum to add amounts.

Option C groups documents by category and sums the amount field, giving total sales per category. Option C calculates average, not total. Option C finds max amount per category. Option C tries to sum in $project, which is invalid.

❓ query_result

intermediate

Filter and count orders above threshold

Which aggregation pipeline returns the count of orders with totalPrice greater than 100 in the orders collection?

MongoDB

db.orders.aggregate([
  { $match: { totalPrice: { $gt: 100 } } },
  { $count: "highValueOrders" }
])

A [{ $match: { totalPrice: { $gt: 100 } } }, { $count: "highValueOrders" }]

B [{ $match: { totalPrice: { $gte: 100 } } }, { $count: "highValueOrders" }]

C [{ $group: { _id: null, count: { $sum: 1 } } }, { $match: { totalPrice: { $gt: 100 } } }]

D [{ $project: { highValue: { $gt: ["$totalPrice", 100] } } }, { $count: "highValueOrders" }]

💡 Hint

Filter first, then count the matching documents.

Option A correctly filters orders with totalPrice > 100 and counts them. Option A groups before filtering, which is incorrect. Option A uses >= 100, not > 100. Option A projects a boolean but does not filter before counting.

📝 Syntax

advanced

Identify the syntax error in this aggregation pipeline

Which option contains a syntax error that will cause the MongoDB aggregation to fail?

MongoDB

db.users.aggregate([
  { $group: { _id: "$city", totalUsers: { $sum: 1 } } },
  { $sort: { totalUsers: -1 } }
])

A [{ $group: { _id: "$city", totalUsers: { $sum: 1 } } }, { $sort: { totalUsers: -1 } }]

B [{ $group: { _id: "$city", totalUsers: { $sum: 1 } } }, { $sort: { totalUsers: "desc" } }]

C [{ $group: { _id: "$city", totalUsers: { $sum: 1 } } }, { $sort: { totalUsers: 1 } }]

D [{ $group: { _id: "$city", totalUsers: { $sum: 1 } } }, { $sort: { totalUsers: 0 } }]

💡 Hint

Check the value used for sorting order in $sort stage.

Option B uses "desc" as a string for sorting order, which is invalid. MongoDB requires 1 for ascending or -1 for descending. Options A, B, and C use numeric values (though 0 in B is ignored but valid syntax).

❓ optimization

advanced

Optimize aggregation pipeline for large datasets

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

A [{ $project: { productId: 1, rating: 1 } }, { $group: { _id: "$productId", avgRating: { $avg: "$rating" } } }]

B [{ $group: { _id: "$productId", avgRating: { $avg: "$rating" } } }, { $match: { avgRating: { $gte: 4 } } }]

C [{ $group: { _id: "$productId", avgRating: { $avg: "$rating" } } }]

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

💡 Hint

Filter out documents missing rating before grouping to reduce workload.

Option D filters documents missing rating before grouping, reducing data processed. Option D groups all documents including those without rating, processing more data than necessary. Option D projects fields but does not filter. Option D filters after grouping, which is less efficient.

🧠 Conceptual

expert

Understanding $facet stage for dashboard reports

Which option best describes the purpose of the $facet stage in MongoDB aggregation pipelines for reporting dashboards?

A It allows running multiple aggregation pipelines in parallel and returns their results in a single document.

B It filters documents based on multiple conditions combined with AND logic.

C It sorts documents by multiple fields in a single stage.

D It groups documents by multiple fields and calculates sums for each.

💡 Hint

Think about how to get different summaries in one query.

The $facet stage lets you run several pipelines at once and get all results together, useful for dashboards needing multiple reports. Other options describe different stages or operations.