Free MongoDB C100DEV Exam Questions

Question 1

You are tasked with developing a high-performance application using MongoDB as your primary database. The application heavily relies on queries that retrieve user profiles based on user names and their location. Given this requirement, you decide to implement a strategy to make these queries more efficient. What strategy should you implement to ensure these queries are covered queries?

A : Use the $project operator in the aggregation pipeline to include only the username and location fields in the result set.

B : Create separate indexes on the username and location fields and include only these fields in the queries.

C : Use the $match operator in the aggregation pipeline to filter documents based on username and location.

D : Create a compound index on the username and location fields and project only these two fields in the queries.

Answer: D

Question 2

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

{

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

"order_number": "ORD-001",

"order_date": ISODate("2022-01-01T00:00:00.000Z"),

"customer": {

"name": "John Doe",

"email": "johndoe@example.com"

},

"items": [

{ "product_name": "Smartphone", "quantity": 2, "price": 800 },

{ "product_name": "Laptop", "quantity": 1, "price": 1500 }

],

"shipping_address": {

"street": "123 Main St",

"city": "New York",

"state": "NY",

"zipcode": "10001"

},

"total_amount": 3000

}

Select an aggregation pipeline that returns the total sales amount by state and month, for orders placed in the year 2022. The result should have the following format:

A : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$group": { "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } }, "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

B : [ { "$group": { "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } }, "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

C : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$group": { "sales_amount": { "$sum": "$total_amount" } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

D : [ { "$match": { "order_date": { "$gte": ISODate("2022-01-01T00:00:00.000Z"), "$lt": ISODate("2023-01-01T00:00:00.000Z") } } }, { "$project": { "state": "$_id.state", "month": "$_id.month", "sales_amount": "$sales_amount", "_id": 0 } }]

Answer: A

Question 3

You have two collections in your MongoDB database: students and grades. The students collection contains the following document:

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"),

"student_id": 1,

"name": "Alice"

}

The grades collection contains the following documents:

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed3"),

"student_id": 2,

"course": "Math",

"grade": "A"

},

{

"_id": ObjectId("64d5b0e5c9d95a76ac3ebed4"),

"student_id": 2,

"course": "Science",

"grade": "B"

}

You run the following aggregation query to retrieve students along with their grades:

db.students.aggregate([

{

$lookup: {

from: "grades",

localField: "student_id",

foreignField: "student_id",

as: "grades_details"

}

])

What will be the structure of the documents returned by this aggregation query for the student with student_id: 1?

A : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": null}

B : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": []}

C : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": [ { "course": "Math", "grade": "A" } ]}

D : { "_id": ObjectId("64d5b0e5c9d95a76ac3ebed2"), "student_id": 1, "name": "Alice", "grades_details": [ { "course": "Math", "grade": "A" }, { "course": "Science", "grade": "B" } ]}

Answer: B

Question 4

Suppose you insert the following documents into a companies collection:

db.companies.insertMany([

{"name": "Facebook"},

{"name": "X"}

])

Select all true statements about this command. (Select three)

A : This command will insert one document into the collection.

B : The command will only insert the first document and skip the second because the _id field is not explicitly provided.

C : This command will insert two documents into the collection.

D : MongoDB will automatically create an _id field for each document and it will be unique.

E : The _id field is not specified in any of these documents.

Answer: C,D,E

Question 5

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