Question 1

In a MongoDB collection named orders, you are tasked with retrieving a subset of documents that fulfill specific criteria. Each document in this collection contains the following fields: customerId, orderId, orderDate, and orderDetails (an embedded document with fields: product, quantity, unitPrice). You are required to fetch all orders made by the customer with a customerId of 1234 on a specific date 2023-04-12, and only return the orderId, orderDate, and product from orderDetails. Which of the following MongoDB queries would accurately fulfill these requirements?

Options :

A :

B :

C :

D :

Answer: A

Question 2

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?

Options :

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 3

Consider a MongoDB database managing e-commerce product data. The database has a collection named products with documents containing the fields product_id, category, price, and reviews. Each reviews field is an array of subdocuments with fields user_id, rating, and comment. You are tasked with optimizing a query that retrieves all products within a specific category that have an average rating higher than 4. The query is frequently used, and performance is critical. Which indexing strategy would best optimize this query?

Options :

A : Create a compound index on category and reviews.rating, with a partial filter expression that only includes documents where the average rating in reviews.rating is greater than 4.

B : Create a multi-key index on reviews.rating.

C : Create a compound index on category and reviews.rating.

D : Create a single index on the category field.

E : Create a compound index on category, reviews.rating, and price.

Answer: A

Question 4

You are designing a MongoDB schema for an e-commerce platform. The platform has three main collections: Products, Orders, and Users.Products Collection: Contains information about each product, such as its name, price, and description.Orders Collection: Contains details about each order, including the userId (referencing the Users collection), an array of productIds (referencing the Products collection), and the total order amount.Users Collection: Contains user details, such as name, email, and address.Which of the following data modeling approaches represents an anti-pattern in MongoDB?

Options :

A : Referencing Orders within the Users collection by storing an array of orderIds.

B : Creating a separate ProductDetails collection and storing all product details there, referencing it from the Products collection.

C : Referencing productIds in the Orders collection instead of embedding full product details.

D : Embedding user details directly in the Orders collection.

Answer: B

Question 5

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

Options :

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

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