NoSQL Online Test

The NoSQL Assessment Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

• Designing and implementing Column-Store system
• Document-Store Id store system implementation
• Creating Key-value solutions
• Optimizing Column-Store solutions
• Developing Document Store IT Solutions
• Implementing Graph-Based Solutions
• Building Key-value store solutions
• Managing data in Column-Store system
• Implementing Document-Store Id store system
• Troubleshooting Key-value solutions

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

• Column-Store system

  The Column-Store system is a database management system that stores data in columns instead of rows. This structure allows for faster data retrieval and analysis, especially for large datasets, as it only reads the columns needed, minimizing disk I/O and improving query performance.

• Document-Store Id store system

  A Document-Store Id store system is a database management system that stores and retrieves data in a document-oriented format, where each document is identified by a unique document identifier (ID). This allows for flexible and scalable data storage, as data is stored in a schema-less manner, accommodating varying data structures and types within each document.

• Key-value solutions

  Key-value solutions are data storage and retrieval systems that use a simple key-value pair structure, where each piece of data is associated with a unique key. This approach enables rapid data access and retrieval, making it ideal for caching, session management, and other scenarios where quick look-up operations are required.

• Graph-Based Solutions

  Graph-based solutions are database management systems that use graph structures to represent and store data. Graphs consist of nodes (representing entities or objects) and edges (representing relationships between nodes). This approach enables efficient querying and analysis of complex relationships and interconnected data, making it suitable for social networks, recommendations, and network analysis.

• Column-Store solutions

  Column-Store solutions are database management systems that store data in a columnar format, rather than the traditional row format. This organization allows for higher compression rates and improved query performance, as only the necessary columns are read during query execution. Column-Store solutions are particularly beneficial for data analytics and reporting, where aggregated queries over large datasets are common.

• Document Store IT Solutions

  Document Store IT Solutions are database management systems that focus on storing and retrieving data in a flexible and self-describing format, such as JSON or XML. This allows for the storage of semi-structured and structured data, facilitating dynamic schema evolution and supporting rapid development and iteration cycles.

• Full list of covered topics

  The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for NoSQL Assessment Test to be based on.

  Column-store design

  Indexing in column stores

  Query optimization in column stores

  Document-store ID retrieval

  Document-store data modeling

  Key-value storage architecture

  Key-value data retrieval methods

  Writing efficient code with column stores

  Designing schema for document stores

  Graph-based data structure

  Graph traversal algorithms

  Graph database integration

  Using key-value stores for caching

  Document-oriented database systems

  Columnar storage compression techniques

  Document-store indexing methods

  Key-value store consistency models

  Querying in graph-based solutions

  Normalization in document stores

  Partitioning in column stores

  Aggregation in column stores

  Sharding in key-value stores

  Concurrent operations in document stores

  Load balancing in graph-based solutions

  Distributed key-value stores

  Data replication in column stores

  Access control in document stores

  Backup and recovery in key-value stores

  Schema evolution in graph-based solutions

  Data migration in document stores

  Data consistency in column stores

  Eventual consistency in key-value stores

  Scalability of graph-based solutions

  Database locking in document stores

  Optimistic concurrency control in key-value stores

  Transaction management in column stores

  Embedded document models in document stores

  Cap theorem and column stores

  Horizontal partitioning in key-value stores

  Traversal algorithms in graph-based solutions

  Capacity planning for document stores

  Denormalization in column stores

  Key-value store durability and fault tolerance

  Graph-based queries optimization

  Database locking in graph-based solutions

  Secondary indexes in document stores

  Data distribution in key-value stores

  Backup and replication strategies in column stores

  Concurrency control in graph-based solutions

  Data modeling in document stores

  Performance optimization in key-value stores

  Data consistency in graph-based solutions

  Query planning and execution in document stores

  Column-store compression algorithms

  Key-value store benchmarks

  Graph visualization and analysis

  Document-store query languages

  Data migration in key-value stores

  Graph database query languages

  Column-store partitioning strategies

  Distributed document stores

  Key-value store consistency models

  Fault tolerance in graph-based solutions

  Schema design in document stores

  Consistency models in column stores

  Distributed key-value store replication

  Security in graph-based solutions

  Sharding strategies for key-value stores

  Data replication in document stores

  Aggregation functions in column stores

  Concurrency control in key-value stores

• NoSQL DataBase Engineer
• Hadoop and NoSQL Operations Engineer
• NoSQL Database Administrator
• NoSQL Platform Engineer

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

• Optimizing performance of Column-Store solutions
• Developing scalable and efficient Document Store IT Solutions
• Designing and modeling Graph-Based Solutions
• Implementing distributed Key-value store solutions
• Analyzing and optimizing data in Column-Store system
• Implementing replication for Document-Store Id store system
• Applying indexing strategies for Key-value solutions
• Designing and implementing distributed Column-Store solutions
• Building secure Document Store IT Solutions
• Implementing traversal algorithms for Graph-Based Solutions