Question 1

Suppose you're designing a MongoDB database for an online ticket booking system where millions of users can concurrently book tickets for various events. To maintain data consistency, you need to ensure that only one user can book a specific seat for an event at a time. Which MongoDB feature would you use to ensure data consistency in this high-concurrency situation?

Options :

A : Multi-document transactions

B : Change Streams

C : Capped Collections

D : Sharding

Answer: A

Question 2

Consider a tasks collection that holds task records. The document structure is as follows:{ "_id": ObjectId("64b64c58ed01c0a5e72dbf5f"), "task": "Review PR", "status": "pending", "assignedTo": "alice"}Two operations are performed concurrently:Operation A:db.tasks.findAndModify({ query: { task: "Review PR", status: "pending" }, remove: true});Operation B:db.tasks.findAndModify({ query: { task: "Review PR", status: "pending" }, update: { $set: { status: "completed" } }, new: true});Operation B starts slightly after Operation A but before Operation A completes. What will be the final state of the tasks collection after both operations have been executed?

Options :

A : The document will be removed from the collection.

B : The document-s status will be updated to "completed".

C : A new document with the status "completed" will be inserted.

D : The document-s status will remain "pending".

Answer: A

Question 3

In the context of MongoDB development, you are creating a new service that is expected to handle high load. You've decided to leverage the MongoDB drivers' connection pooling capability. What best describes connection pooling and its advantages?

Options :

A : Connection pooling ensures secure data transfer between the client and the server by encrypting all connections in the pool.

B : Connection pooling allows a client to connect to a database only when a data transfer operation occurs, reducing the memory requirements.

C : Connection pooling involves maintaining a "pool" of open database connections, which can be reused for multiple requests, improving performance.

D : Connection pooling refers to the storage of multiple connection strings for redundancy, increasing system reliability.

Answer: C

Question 4

Which of the following stages do you need to use to randomly select 10 documents from a specific collection?

Options :

A : { $rand: { size: 10 } }

B : { $sample: { get: 10 } }

C : { $sampleRate: { size: 10 } }

D : { $sample: { size: 10 } }

Answer: D

Question 5

Consider the following documents in a MongoDB collection orders:{ "_id": 1, "customer_id": 101, "item": "apple", "price": 0.5, "quantity": 100, "date": ISODate("2022-01-01T00:00:00Z")},{ "_id": 2, "customer_id": 102, "item": "banana", "price": 0.25, "quantity": 50, "date": ISODate("2022-01-02T00:00:00Z")},{ "_id": 3, "customer_id": 101, "item": "orange", "price": 0.75, "quantity": 75, "date": ISODate("2022-01-03T00:00:00Z")}What is the aggregation pipeline to group the documents by "customer_id" and calculate the sum of the "quantity" field for each group, sorted in descending order by the sum of the "quantity" field?

Options :

A : db.runCommand({ aggregate: "orders", group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } }, sort: { total_quantity: -1 }})

B : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: 1 } }])

C : db.orders.aggregate([ { $groupBy: { customer_id: "$customer_id" }, total_quantity: { $sum: "$quantity" } }, { $sort: { total_quantity: 1 } }])

D : db.orders.aggregate([ { $group: { _id: "$customer_id", total_quantity: { $sum: "$quantity" } } }, { $sort: { total_quantity: -1 } }])

Answer: D

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