Introduction to Computer Programming: Part 1, PRL Quiz 1.

Read the paper following paper to answer the quiz below.

Eyada, M. M., Saber, W., El Genidy, M. M., & Amer, F. (2020). Performance evaluation of IoT data management using MongoDB versus MySQL databases in different cloud environments. IEEE access, 8, 110656-110668.

Question 1: Which of the following best describes the role of cloud service providers in IoT technology?

They provide only storage services for IoT data.

They only provide programming languages for IoT applications.

They offer business applications, network services, and infrastructure for cloud users.

They are responsible for manufacturing IoT devices.

Question 2:Which statement accurately compares MongoDB and MySQL in the context of IoT data management?

MySQL is more efficient than MongoDB in handling large volumes of IoT data due to its structured query language.

MongoDB is a document-oriented Non-relational database, while MySQL is a Relational Database Management System (RDBMS).

MongoDB and MySQL both use the same data storage format and are equally efficient in handling IoT data.

Both MongoDB and MySQL are primarily used for cloud-native applications only.

Question 3: What is the primary advantage of using Node.JS in server-side IoT data management?

It is mainly used for its compatibility with SQL databases.

Node.JS is favored for its event-driven, asynchronous programming suitable for network services.

Node.JS is the most resource-efficient programming language available.

It is the only programming language that supports database management.

Question 4: According to the section, what was a significant finding in the research comparing SQL and NoSQL DBMSs based on IoT data structure?

The experiments conclusively proved that MongoDB is the only viable option for IoT data management.

SQL databases always outperformed NoSQL databases in all metrics.

The hybrid database model using MongoDB and MySQL showed improved performance over using MySQL alone.

NoSQL databases were found to be less efficient in handling IoT data compared to SQL databases.

Question 5: What is the primary objective of the experiments proposed in this paper?

To analyze the security aspects of IoT data management.

To design a new type of database system specifically for IoT applications.

To determine the cost-effectiveness of different IoT devices.

To evaluate and compare the performance of SQL and NoSQL databases on IoT data.

Question 6: Which of the following is NOT a part of the three main parts of the evaluation conducted in the experiments?

Proposing a prediction model for database latency estimation.

Assessing the environmental impact of IoT data management.

Examining the impact of increasing workloads on databases.

Testing the effect of cloud instance capabilities on database performance.

Question 7: In the context of this paper, how is the pollution database modified for use in the experiments?

By limiting the database to indoor air pollution data only.

By integrating image and audio data with sensor data.

By partitioning the database into sensors, location coordinates, and timestamp parts.

By adding more sensor types and increasing data redundancy.

Question 8. What distinguishes the MongoDB setup from the MySQL setup in these experiments?

Only MySQL is capable of handling the IoT sensor data efficiently.

MongoDB and MySQL both use referential integrity constraints in the same way.

MongoDB exclusively uses SQL for data management, while MySQL does not.

MongoDB uses reference, embedded, and hybrid models for data set management, which is not the case with MySQL.

Question 9. What was a key finding regarding the performance of MySQL and MongoDB under increasing workloads?

Neither MySQL nor MongoDB showed any performance changes under increasing workloads.

MySQL and MongoDB both showed similar performance degradation under increasing workloads.

MySQL exhibited a greater loss in performance compared to MongoDB under increasing workloads.

Increasing workloads led to a greater loss in performance for MongoDB compared to MySQL.

Question 10. Which model of MongoDB was shown to be more efficient in terms of database size according to the paper's results?

The embedded model.

The reference model.

All models showed equal efficiency.

Question 11. In the context of estimating database latency, how did the two regression models compare?

Both Linear and Non-linear Regressions were equally effective.

Linear Regression was found to be more accurate than Non-linear Regression.

Non-linear Regression was more effective than Linear Regression.

The paper did not compare Linear and Non-linear Regression models.

Question 12. What is suggested as future work in the paper?

Focusing only on improving the performance of MongoDB.

Discontinuing the use of MySQL in favor of more advanced databases.

Implementing a hybrid model between MongoDB and MySQL.

Limiting the comparison to small-data workloads only.