Question 1

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 2

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 3

Given a movies collection where each document has the following structure:{ _id: ObjectId('573a1390f29313caabcd60e4'), genres: [ 'Short', 'Comedy', 'Drama' ], title: 'The Immigrant', year: 1917, imdb: { rating: 7.8, votes: 4680, id: 8133 }, countries: [ 'USA' ]}Which of the following queries will find all the movies that have more votes than the year in which they were released?

Options :

A : db.movies.find({ $expr: { $gt: ["imdb.votes", "year"] } } )

B : db.movies.find({ $expr: { $lt: ["$imdb.votes", "$year"] } } )

C : db.movies.find({ $gt: ["$imdb.votes", "$year"] } )

D : db.movies.find({ $expr: { $gt: ["$imdb.votes", "$year"] } } )

Answer: D

Question 4

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": "[email protected]" }, "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:

Options :

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 5

We have a movies collection with the following document structure:{ _id: ObjectId("573a1390f29313caabcd6223"), genres: [ 'Comedy', 'Drama', 'Family' ], title: 'The Poor Little Rich Girl', released: ISODate("1917-03-05T00:00:00.000Z"), year: 1917, imdb: { rating: 6.9, votes: 884, id: 8443 }},{ _id: ObjectId("573a13e3f29313caabdc08a4"), genres: [ 'Horror', 'Thriller' ], title: 'Mary Loss of Soul', year: 2014, imdb: { rating: '', votes: '', id: 2904798 }}We need to use Aggregation Framework to calculate the following aggregates:average imdb ratingminimum imdb ratingmaximum imdb ratingExpected output:[ { _id: null, avg_rating: 6.6934040649367255, min_rating: 1.6, max_rating: 9.6 }]Please note that some documents have "" (empty string) for the field "imdb.rating". Exclude these documents before aggregation.Which pipeline should you use?

Options :

A : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { $avg: "imdb.rating" }, min_rating: { $min: "imdb.rating" }, max_rating: { $max: "imdb.rating" } }}]

B : [{ $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } }}]

C : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } }}]

D : [{ $match: { "imdb.rating": { $ne: "" } }}, { $group: { _id: null, avg_rating: { avg: "$imdb.rating" }, min_rating: { min: "$imdb.rating" }, max_rating: { max: "$imdb.rating" } }}]

Answer: C

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