100% Free MongoDB C100DEV Practice Test Questions

Question 1

What command can you use to create a capped collection named latest_news that will be limited to 3 documents and 10,000 bytes?

A : db.createCollection('latest_news', {'size': 10000, 'max': 3})

B : db.createCollection('latest_news', {'capped': true, 'size': 10000, 'max': 3})

C : db.createCollection('latest_news', 'capped': true, 'size': 10000, 'max': 3)

D : db.createCollection('latest_news', {'capped': true, 'max': 3})

Answer: B

Question 2

You are a MongoDB Associate Developer and need to demonstrate how to load the Atlas Sample Dataset into your MongoDB Atlas cluster. Your manager has asked you to guide a junior developer through this process to ensure they can independently load the dataset. What steps should the junior developer follow to successfully load the Atlas Sample Dataset?

A : In MongoDB Atlas, navigate to the "Clusters" tab, click on the "Load Sample Dataset" button, and wait for the dataset to be fully loaded.

B : In MongoDB Compass, connect to your cluster, go to the "Import Data" tab, and import the sample dataset by uploading the sample-dataset.json file.

C : Use the MongoDB Atlas API to send a POST request to the /loadSampleDataset endpoint, specifying your cluster ID in the request body.

D : Open the MongoDB Shell, connect to your cluster, and run the command mongoimport --db sample --collection datasets --file sample-dataset.json.

Answer: A

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

You have a MongoDB collection named employees that contains documents with fields name, department, and salary. You want to update the salary of all employees in the "Marketing" department to $75,000. The original collection looks like this:

[

{ "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 },

{ "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 },

{ "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 62000 },

{ "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }

]

You run the following MongoDB update operation:

db.employees.updateMany(

{ "department": "Marketing" },

{ $set: { "salary": 75000 } }

)

What will the collection look like after this update operation?

A : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

B : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 62000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 62000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

C : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 75000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 55000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

D : [ { "_id": 1, "name": "Alice", "department": "Marketing", "salary": 60000 }, { "_id": 2, "name": "Bob", "department": "Sales", "salary": 75000 }, { "_id": 3, "name": "Charlie", "department": "Marketing", "salary": 75000 }, { "_id": 4, "name": "David", "department": "Engineering", "salary": 70000 }]

Answer: C

Question 5

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

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