Q56 — AWS SAA-C03 Ch.12

Question 56 of 100 | ← Chapter 12

Q856. A company has 5 TB of datasets. The datasets consist of 1 million user profiles and 10 million connections. The user profiles have connections as many-to-many relationships. The company needs a performance efficient way to find mutual connections up to five levels.Which solution will meet these requirements?

Correct Answer: B. Use Amazon Neptune to store the datasets with edges and vertices. Query the data to find connections.

Explanation

To efficiently find mutual connections up to five levels among 1 million user profiles and 10 million connections, the recommended solution is:B. Use Amazon Neptune to store the datasets with edges and vertices. Query the data to find connections.Here's why:Option B suggests using Amazon Neptune, a purpose-built graph database service, to store the datasets with edges and vertices. Graph databases are specifically designed to handle complex relationships and queries within highly connected data.In this scenario, the user profiles and connections can be modeled as vertices and edges, respectively, in a graph database. By leveraging the graph structure, Amazon Neptune provides efficient traversal and querying capabilities to find mutual connections up to five levels. The graph database's native support for many-to-many relationships makes it well-suited for this use case.Amazon Neptune supports the Gremlin query language and the RDF/SPARQL query language, providing flexible options for querying the data and discovering connections efficiently. You can use graph traversal algorithms to navigate the graph and find mutual connections within the desired depth limit.Option A suggests using an Amazon S3 bucket to store the datasets and using Amazon Athena to perform SQL JOIN queries. While Amazon Athena is a powerful serverless query service for data stored in Amazon S3, it is optimized for running SQL queries on structured data rather than traversing complex relationships in a graph-like manner. Using SQL JOIN queries may not be efficient for finding mutual connections up to five levels.Option C suggests using an Amazon S3 bucket to store the datasets and using Amazon QuickSight to visualize connections. While QuickSight is a business intelligence tool that can provide visualizations, it does not offer the same level of graph querying capabilities as a dedicated graph database like Amazon Neptune. It may not be efficient for finding mutual connections in a many-to-many relationship.Option D suggests using Amazon RDS to store the datasets with multiple tables and performing SQL JOIN queries to find connections. While SQL JOIN queries can handle relational data, they may not be the most performant solution for finding connections within a graph-like structure with many-to-many relationships.In summary, the recommended solution is to use Amazon Neptune as a purpose-built graph database to store the datasets and query the data efficiently to find mutual connections up to five levels. This approach leverages the graph database's optimized traversal and querying capabilities for highly connected data, providing an efficient solution for the given requirements.