Free MongoDB C100DEV Exam Questions

Question 1

You manage a MongoDB collection called inventory that stores documents for a store's inventory. Each document has the fields item, category, and quantity. You want to ensure that an item is always present in the collection, even if it needs to be inserted. Specifically, if the item "notebook" in the "Stationery" category does not exist, it should be inserted with a quantity of 50. If it does exist, its quantity should be updated to 50. You plan to use the following command:

db.inventory.updateOne(

{ "item": "notebook", "category": "Stationery" },

{ $set: { "quantity": 50 } },

{ upsert: true }

)

What will happen after executing this command if there is no "notebook" item in the "Stationery" category in the inventory collection?

A : A new document will be inserted with only quantity: 50, as other fields (item and category) are not mentioned in the $set operator.

B : Nothing will happen because the $set operator only updates existing documents and cannot insert new ones.

C : A new document will be inserted with quantity: 50, but the item field will be set to null.

D : A new document will be inserted with item: "notebook", category: "Stationery", and quantity: 50.

Answer: D

Question 2

In MongoDB, which statement accurately describes a typical task for a MongoDB developer related to schema validation?

A : Schema validation enables the creation of complex relationships between multiple collections.

B : Schema validation guarantees the uniqueness of a specific field across all documents in a collection.

C : Schema validation ensures the presence of required fields and their data types within a document.

D : Schema validation provides real-time data synchronization across multiple MongoDB instances.

Answer: C

Question 3

Consider a MongoDB database containing a collection of documents representing product information for an e-commerce website. The documents have the following structure:

{

"_id": ObjectId("5f95a1d11a12b400001b75c0"),

"product_name": "Smartphone",

"brand": "Apple",

"price": 800,

"categories": [ "Electronics", "Smartphones" ],

"reviews": [

{ "username": "user1", "rating": 4, "comment": "Great product!" },

{ "username": "user2", "rating": 5, "comment": "Excellent!" },

{ "username": "user3", "rating": 3, "comment": "Good but overpriced." }

]

}

Select the MongoDB aggregation pipeline that returns the average rating of all products grouped by brand. The result should include only brands with an average rating greater than or equal to 4. The output should have the following format:

A : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }]

B : [ { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

C : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

D : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

Answer: D

Question 4

In your database there is a collection named trips with the following document structure:

{

'_id': ObjectId("572bb8222b288919b68abf6d"),

'trip_duration': 858,

'start_station id': 532,

'end_station_id': 401,

'bike_id': 17057,

'start_station_loc': { type: 'Point', coordinates: [ -73.960876, 40.710451 ] },

'end_station_loc': { type: 'Point', coordinates: [ -73.98997825, 40.72019576 ] },

'start_time': ISODate("2016-01-01T00:09:31.000Z"),

'stop_time': ISODate("2016-01-01T00:23:49.000Z")

}

How can you extract all trips from this collection ended at stations that are to the west of the -73.5 longitude coordinate?

A : db.trips.find( { 'end_station_loc.coordinates.0': { $gt: -73.5 } } )

B : db.trips.find( { 'coordinates': { $lt: -73.5 } } )

C : db.trips.find( { 'end_station_loc.coordinates.0': { $lt: -73.5 } } )

D : db.trips.find( { 'end_station_loc.coordinates.1': { $lt: -73.5 } } )

Answer: C

Question 5

You are developing a MongoDB application where write performance is a critical requirement. Given a scenario where your application primarily performs insert operations and rarely performs updates or deletes, which of the following MongoDB features would best optimize this heavy-write workload?

A : Change Streams

B : Capped Collections

C : Aggregation Pipeline

D : Sharding

Answer: D