Data Management and NoSQL Systems Quiz

Which type of data does a data lake primarily store?

What is the main advantage of synthetic training data for AI systems?

In what aspects do NoSQL systems excel compared to relational databases?

What is the primary advantage of relational databases over NoSQL systems?

What type of data storage solution is recommended for a university library's historic map digitization project?

What is the main advantage of a document store over a key-value store?

In a DHT implementation like Chord, what is the purpose of finger tables?

What is the routing complexity of P2P systems like Chord?

What does ETL stand for in the context of data management?

What does a data lake struggle with in data management?

What are some reasons why data-related tasks take so much time and effort in typical machine learning projects?

Which best summarizes the 4V’s of Big Data?

How can the 4V’s of Big Data be addressed by current technology?

In a DHT using finger tables with a hash range from 0 to 255 organized as a hash ring, if Node 1 covers the hash range 3-55, what would be the entry for distance 32 in Node 1's Chord-style finger table?

Briefly outline the difference between consistency in the CAP theorem and consistency in ACID.

A new database system claims that it can recover from a fatal hard drive failure by rebuilding all lost data from a log file stored on a different drive, and thus would be available (in the sense of the CAP theorem). Briefly discuss this claim.

Can the Two-Phase Commit Protocol recover from a worker who sent a 'ready' answer to a 'prepare', but never answered with an 'acknowledge' after a 'commit'. Why (not)?

Why does a system like Amazon Dynamo use a Vector Clock instead of regular time stamps?

In a replicated data storage scenario, a master-slave setup for each replica using locks for write operations would ensure that 'read-your-own-write' conflicts will not happen.

Data-related tasks in typical machine learning projects are usually generalizable and do not require custom made solutions.

The 4V's of Big Data include Volume, Velocity, Variety, and Veracity.

The 4V's of Big Data can be fully addressed by current technology.

The ability to ingest data at high rates is not a concern when dealing with Big Data.

Data aggregation is a task specific to a given problem domain and cannot be solved in a general fashion.

Data cleaning is not a time-consuming task in machine learning projects.

Data labelling is a task specific to a given problem domain and cannot be solved in a general fashion.

Data uncertainty and quality are not important considerations in dealing with Big Data.

The primary concern with Big Data is related to the size of data to store.

Most data-related tasks in machine learning projects rely on manual labor.

Amazon DynamoDB uses finger tables for routing in a Chord-style DHT.

A DHT using finger tables with a hash range from 0 to 255, organized as a hash ring, can have a maximum of 8 nodes.

Consistency in the CAP theorem refers only to consistency of replicas, while ACID consistency refers to overall data consistency when faced with potentially complex transactions.

A new database system claiming to recover from a fatal hard drive failure by rebuilding all lost data from a log file would be fully available according to the CAP theorem.

The Two-Phase Commit Protocol can recover from a worker who sent a 'ready' answer to a 'prepare', but never answered with an 'acknowledge' after a 'commit'.

Amazon Dynamo uses a Vector Clock instead of regular time stamps because Vector Clocks are more efficient for timestamping data.

In a replicated data storage scenario, a master-slave setup for each replica using locks for write operations would ensure that 'read-your-own-write' conflicts will not happen.

A Chord-style finger table for Node 1 would have the entry for distance 32 as Node 3.

NoSQL systems excel compared to relational databases in terms of data consistency and atomicity.

A document store has the main advantage of better scalability over a key-value store.

Data lakes are designed to have a solid central understanding of data semantics.

A document store would have the smallest impedance mismatch in the scenario described, given the majority of the application is written in JavaScript and Python.

Finger tables in a DHT implementation like Chord serve the purpose of quickly locating the physical machine holding the requested data and maintaining routing complexity.

NoSQL systems struggle with availability and replication compared to relational databases.

ETL stands for Extract, Transform, Load, and is a pipeline workflow for transferring data between systems.

A data lake is a single store of diverse, big, and varied data with a solid central understanding of data semantics.

Relational databases have advantages in ACID transactions, powerful SQL query language, and flexible data model suited for tabular data.

A document store is not suggested as the data storage solution for a university library's historic map digitization project, matching the data model and queries well.

Synthetic training data for AI systems suffers from unclear semantic reliability and limited applicability.

P2P systems like Chord use finger tables to ensure each node only needs a node state of O(log n) while maintaining a routing complexity of O(log n).

Data-related tasks in typical machine learning projects usually rely on ______ labor

The 4V's of Big Data include Volume, Velocity, Variety, and ______

The ability to ingest data at high rates is a concern when dealing with Big ______

A document store would have the smallest impedance mismatch in the scenario described, given the majority of the application is written in JavaScript and ______

Consistency in the CAP theorem refers only to consistency of replicas, while ACID consistency refers to overall data consistency when faced with potentially complex ______

In what aspects do NoSQL systems excel compared to ______ databases?

Data labelling is a task specific to a given problem domain and cannot be solved in a general ______

The 4V's of Big Data can be fully addressed by current ______

In a DHT implementation like Chord, what is the purpose of finger ______?

ETL stands for Extract, ______, Load, and is a pipeline workflow for transferring data between systems

Amazon DynamoDB uses finger tables for routing in a Chord-style DHT

A Chord-style finger table for Node 1 would have the entry for distance 32 as Node 3

In a DHT using finger tables with a hash range from 0 to 255, organized as a hash ring, can have a maximum of 8 nodes

The routing complexity of P2P systems like Chord

The 4V's of Big Data include Volume, Velocity, Variety, and Veracity

The main advantage of synthetic training data for AI systems

ETL stands for Extract, Transform, Load, and is a pipeline workflow for transferring data between systems

In a DHT implementation like Chord, what is the purpose of finger tables

NoSQL systems excel compared to relational databases in terms of data consistency and atomicity

A document store has the main advantage of better scalability over a key-value store

ETL stands for Extract, Transform, Load, and is a pipeline workflow for transferring data between ______

NoSQL systems offer potential scalability, global distribution, higher performance, and focus on availability and replication compared to ______ databases

A document store is suggested as the data storage solution for a university library's historic map digitization project, matching the data model and ______ well

A document store would have the smallest impedance mismatch in the scenario described, given the majority of the application is written in JavaScript and ______

The potential disadvantages of a document store over a key-value store include complexity and potential performance ______

Finger tables in a DHT implementation like Chord serve the purpose of quickly locating the physical machine holding the requested data and maintaining routing ______

P2P systems like Chord use finger tables to ensure each node only needs a node state of O(log n) while maintaining a routing complexity of ______

Synthetic training data for AI systems has advantages in generating large amounts of data quickly, but suffers from unclear semantic reliability and limited ______

Current technology can handle Volume and Velocity well, but struggles with Variety and ______ in data management

Data management for a large enterprise application using SOA involves several smaller co-located databases communicating via interfaces, unlike traditional enterprise database ______

Match the following data-related tasks with their descriptions:

Match the 4V's of Big Data with their descriptions:

Match the following statements with their accuracy regarding the 4V's of Big Data:

Match the data-related tasks with their reliance on manual labor:

Match the 4V's of Big Data with the challenges they pose for data management:

Match the following data management concepts with their descriptions:

Match the following advantages with their corresponding data storage solutions:

Match the following data management scenarios with their corresponding data storage solutions:

Match the following P2P system components with their functions:

Match the following data management challenges with their corresponding technology capabilities:

Match the following concepts with their respective explanations:

Match the following scenarios with their respective outcomes:

Match the following concepts with their respective explanations:

Match the following statements with their respective explanations:

Match the following scenarios with their respective outcomes:

Match the following concepts with their respective explanations:

Match the following statements with their respective explanations:

Match the following programming languages with their primary usage:

Match the following programming languages with their primary usage:

Match the following programming languages with their primary usage: