Question 1

In a MongoDB database for an online bookstore, you're designing a data model to handle books, authors, and reviews efficiently, considering a complex scenario involving relationships and potential data skew due to varying numbers of reviews per book. Given the requirements:Each book can have multiple authors.Each author can write multiple books.Books can have a large number of reviews, but most have a few or none, creating potential data skew.The application frequently queries books with their authors and a sample of reviews to avoid performance issues due to data skew.How should you model the relationships among books, authors, and reviews to optimize for query performance and data management?

Options :

A : Embed authors and a fixed number of top reviews in each book document.

B : Use a separate collection for reviews and link them to books with a book ID. Authors and books are embedded within each other-s documents.

C : Reference authors and reviews in the book document with foreign keys.

D : Embed books within author documents, including all reviews for each book.

Answer: A

Question 2

A MongoDB data modeler needs to optimize a collection named orders to efficiently query documents based on the status field, which can have values like 'pending', 'completed', and 'canceled'. The collection has millions of documents, and the queries should return results quickly even under heavy load. Which of the following approaches best achieves this requirement while maintaining the collection's scalability and performance?

Options :

A : Create a separate collection for each status value and distribute the documents accordingly, querying the relevant collection based on the status.

B : Index the status field using a single field index, ensuring queries filtering by 'status' are more efficient.

C : Convert the status field into an array and use a multi-key index, ensuring multiple status values can be queried simultaneously.

D : Use a hashed index on the status field to evenly distribute the documents across the collection.

Answer: B

Question 3

In designing a database schema for a MongoDB application that handles user-generated content, such as posts and comments, a data modeler is considering the most efficient way to store comments related to each post to optimize read operations, as the application experiences significantly more reads than writes. The application requires frequent retrieval of posts along with their associated comments. What pattern should the data modeler use to store comments in relation to posts, and why?

Options :

A : Reference Pattern

B : Subset Pattern

C : Bucket Pattern

D : Normalized Pattern

E : Embedded Pattern

Answer: E

Question 4

In a MongoDB database tracking e-commerce orders, each order document includes fields for customer ID, order date, and a status that changes over time. To automatically purge orders older than two years and efficiently support queries for recent orders by customer ID, which combination of indexes would be most appropriate?

Options :

A : A compound index on customer ID and order date, and a TTL index on the status field.

B : A single-field index on the customer ID and a Time-To-Live (TTL) index on the order date.

C : A text index on the customer ID for efficient search and a TTL index on the order date.

D : A hashed index on customer ID and a TTL index on the order date.

Answer: B

Question 5

To improve the performance of queries on a logEntries collection, a MongoDB developer needs to find and analyze slow queries that have been executed. Which command should be used to identify and provide details on slow operations for analysis?

Options :

A : db.adminCommand({ "getLog": "global" })

B : db.setProfilingLevel(2, { "slowms": 100 })

C : db.logEntries.find({ "slowQuery": { $exists: true } })

D : db.logEntries.aggregate([ { $currentOp: { "slowOp": true } } ])

Answer: A

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