JNTU
JNTU B.Tech III DATA ANALYTICS FOR IOT Previous Question Paper (January/February - 2023 - R18)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
Define IoT.
Answer
The Internet of Things (IoT) is a network of physical objects or 'things' embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.
What is the need of data analytics?
Answer
Data analytics is necessary to transform raw, massive volumes of data generated by IoT devices into actionable insights. It enables predictive maintenance, improves operational efficiency, supports informed decision-making, and helps in identifying patterns or anomalies that are not visible through manual monitoring.
List the telematic devices.
Answer
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What are the different types of sensors used in IoT?
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Give the features of Parquet.
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What is cloud infrastructure?
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Define data visualization.
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List the features of the Tableau.
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Define precision, recall.
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What is machine learning?
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Part B
Describe Internet of Things architectures with suitable diagrams.
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Explain different types of data analytics and their application to IoT environment.
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Discuss the protocol stack used in internet of things network and the problems solved by network protocols.
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Make a comparison of Zigbee network with 6LoWPAN for IoT.
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Explain the steps followed in encapsulation process and the goal of encapsulation analytics.
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Does private subnets provide security? Justify your answer.
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Discuss the methods used to build security into analytics by cloud service providers.
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Explain the techniques to understand the data quality of IoT devices.
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Discuss the concept of regression used in statistical analysis and R support for statistical analysis.
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Describe feature engineering with IoT data.
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Demonstrate different validation methods used for IoT data.
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Discuss the importance of bias and variance.
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