FutureSkills and Cyber Security

 Introduction to FutureSkills

FutureSkills is meant for IT professionals. It will help them to upgrade their skills. With the increase use of technologies, there is a need to realign the approaches of the industry.

Future skills refer to the competencies and abilities that will be essential for individuals to thrive in the rapidly changing job market and society of the future. The skills are influenced by technological advancements, global trends, and evolving work environments.

Introduction to Internet of Things (IoT)

The Internet of Things (IoT) refers to a network of interconnected devices that communicate and exchange data with each other through the internet. These devices, often embedded with sensors, software, and other technologies, can range from everyday household items to sophisticated industrial tools.

Key Components of IoT

1. Devices/Sensors:

These are the physical objects that collect data from their environment.

Examples: Thermostats, smartwatches, industrial machines.

2. Connectivity:

The data collected by devices needs to be transmitted to other devices or systems.

Connectivity options: Wi-Fi, Bluetooth, cellular networks, satellite, etc.

3. Data Processing:

The data collected needs to be processed to make it meaningful.

This can be done on the device (edge computing) or sent to the cloud for processing.

4. User Interface:

Users need a way to interact with the IoT system, receive notifications, and control devices.

Examples: Mobile apps, web dashboards, voice assistants.

Applications of IoT

1. Smart Homes:

Home automation systems that control lighting, heating, air conditioning, security, and appliances.

Examples: Smart thermostats, smart locks, smart lighting systems.

2. Healthcare:

Wearable devices and remote monitoring systems to track patient health and manage chronic diseases.

Examples: Fitness trackers, remote patient monitoring devices.

3. Industrial IoT (IIoT):

Use of IoT in manufacturing and industrial processes for predictive maintenance, asset tracking, and automation.

Examples: Connected machinery, supply chain monitoring.

4. Agriculture:

IoT applications in farming for precision agriculture, including monitoring soil moisture, weather conditions, and crop health.

Examples: Smart irrigation systems, drone-based crop monitoring.

5. Transportation:

IoT in transportation for vehicle tracking, fleet management, and smart traffic control.

Examples: Connected cars, smart traffic lights.

6. Smart Cities:

Urban infrastructure management including smart lighting, waste management, and traffic management.

Examples: Smart parking systems, waste collection sensors.

Benefits of IoT

1. Efficiency:

Automates routine tasks and optimizes processes, reducing manual intervention.

2. Cost Savings:

Predictive maintenance and efficient resource use can lead to significant cost reductions.

3. Improved Decision Making:

Real-time data collection and analysis enable better and faster decision-making.

4. Enhanced Customer Experiences:

Personalization and improved service delivery through connected devices.

Challenges of IoT

1. Security:

IoT devices can be vulnerable to hacking and cyber-attacks, leading to data breaches and privacy issues.

2. Interoperability:

Different devices and systems need to communicate seamlessly, which can be challenging due to varying standards and protocols.

3. Data Management:

The sheer volume of data generated by IoT devices requires robust data storage, processing, and management solutions.

4. Privacy:

Collecting and analyzing personal data raises concerns about user privacy and consent.

Future of IoT

1. 5G Connectivity:

The rollout of 5G networks will provide faster, more reliable connections, enhancing IoT capabilities.

2. Edge Computing:

Processing data closer to where it is generated (at the edge) will reduce latency and improve real-time decision-making.

3. AI and Machine Learning:

Integrating AI and ML with IoT will enable more advanced data analytics, predictive maintenance, and automation.

4. Increased Adoption:

Continued growth in IoT applications across various industries, from healthcare to smart cities.

Big Data Analytics

Big Data Analytics refers to the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information.

It involves the use of advanced analytics techniques and tools to extract insights and make informed decisions.

1. Data Sources:

Structured Data: Data that is organized in a fixed format, such as databases.

Unstructured Data: Data that does not have a predefined format, such as emails, social media posts, videos, and images.

Semi-structured Data: Data that does not conform to a rigid structure but has some organizational properties, such as XML and JSON files.

2. Data Collection:

Gathering data from various sources such as sensors, social media, transactional systems, and external data providers.

3. Data Storage:

Databases: Traditional relational databases for structured data.

Data Lakes: Storage repositories that can hold vast amounts of raw data in its native format until it is needed.

Data Warehouses: Systems used for reporting and data analysis, storing integrated data from multiple sources.

4. Data Processing:

Batch Processing: Handling large volumes of data at once, often using tools like Hadoop.

Stream Processing: Real-time data processing, using tools like Apache Kafka and Apache Storm.

5. Data Analysis:

Descriptive Analytics: Summarizing past data to understand what has happened.

Diagnostic Analytics: Investigating why something happened.

Predictive Analytics: Using statistical models and machine learning to predict future outcomes.

Prescriptive Analytics: Recommending actions based on the analysis.

6. Data Visualization:

Creating visual representations of data to communicate insights effectively, using tools like Tableau, Power BI, and D3.js.

Applications of Big Data Analytics

1. Business and Marketing:

Customer segmentation, personalized marketing, sales forecasting, and sentiment analysis.

2. Healthcare:

Predictive analytics for disease outbreaks, personalized medicine, and optimizing hospital operations.

3. Finance:

Fraud detection, risk management, algorithmic trading, and customer analytics.

4. Retail:

Inventory management, demand forecasting, and customer behavior analysis.

5. Manufacturing:

Predictive maintenance, quality control, and supply chain optimization.

6. Government:

Public safety, smart city initiatives, and policy making.

7. Telecommunications:

Network optimization, customer churn analysis, and service personalization.

Benefits of Big Data Analytics

1. Improved Decision Making:

Data-driven insights help organizations make informed decisions.

2. Increased Efficiency:

Automation and optimization of processes lead to cost savings and operational efficiency.

3. Competitive Advantage:

Leveraging data for strategic advantage over competitors.

4. Enhanced Customer Experience:

Personalizing products and services based on customer data.

5. Risk Management:

Identifying and mitigating risks through predictive analytics.

Challenges of Big Data Analytics

1. Data Quality:

Ensuring the accuracy, completeness, and consistency of data.

2. Data Integration:

Combining data from diverse sources.

3. Scalability:

Handling the volume, velocity, and variety of big data.

4. Security and Privacy:

Protecting sensitive data and complying with regulations.

5. Skill Gaps:

Finding and retaining skilled data scientists and analysts.

Tools and Technologies

1. Hadoop:

An open-source framework for distributed storage and processing of large data sets.

2. Spark:

A fast and general-purpose cluster computing system for big data processing.

3. Kafka:

A distributed streaming platform for building real-time data pipelines and applications.

4. NoSQL Databases:

Databases like MongoDB and Cassandra that handle large volumes of unstructured data.

5. Machine Learning Libraries:

Libraries such as TensorFlow, Scikit-Learn, and PyTorch for building predictive models.

6. Visualization Tools:

Tools like Tableau, Power BI, and D3.js for creating data visualizations.

Future Trends in Big Data Analytics

1. Artificial Intelligence and Machine Learning:

Increasing use of AI and ML to automate and enhance data analytics.

2. Edge Computing:

Processing data closer to where it is generated to reduce latency and bandwidth usage.

3. Data-as-a-Service (DaaS):

Offering data and analytics services through cloud platforms.

4. Increased Focus on Data Privacy:

Adhering to stricter data protection regulations and ensuring privacy.

5. Real-time Analytics:

Growing demand for real-time data processing and analysis.

Cloud Computing

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale.

Characteristics of Cloud Computing

1. On-Demand Self-Service:

Users can provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.

2. Broad Network Access:

Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).

3. Resource Pooling:

The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand.

4. Rapid Elasticity:

Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand.

5. Measured Service:

Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).

Types of Cloud Computing

1. Public Cloud:

Services are delivered over the public internet and shared across multiple organizations.

Examples: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP).

2. Private Cloud:

Cloud computing resources used exclusively by a single business or organization. It can be physically located on the company’s on-site datacenter or hosted by a third-party service provider.

Example: VMware vSphere.

3. Hybrid Cloud:

Combines public and private clouds, bound together by technology that allows data and applications to be shared between them.

Example: Microsoft Azure Stack.

4. Community Cloud:

Infrastructure shared by several organizations for a shared purpose and may be managed by them or a third party.

Example: Government cloud services for different departments.

Service Models of Cloud Computing

1. Infrastructure as a Service (IaaS):

Provides virtualized computing resources over the internet. It includes servers, storage, and networking hardware along with the hypervisor or virtualization layer.

Examples: Amazon EC2, Google Compute Engine, Microsoft Azure Virtual Machines.

2. Platform as a Service (PaaS):

Delivers hardware and software tools over the internet. Typically, these are tools needed for application development.

Examples: Google App Engine, Microsoft Azure App Service, AWS Elastic Beanstalk.

3. Software as a Service (SaaS):

Delivers software applications over the internet, on a subscription basis. Users can access the software from any device, typically using a web browser.

Examples: Google Workspace, Microsoft Office 365, Salesforce.

Benefits of Cloud Computing

1. Cost Efficiency:

Reduces the capital expense of buying hardware and software and setting up and running on-site datacenters.

2. Scalability:

Easily scale up or down to accommodate the needs of your business.

3. Performance:

Major cloud services run on a worldwide network of secure datacenters, which are upgraded to the latest generation of fast and efficient computing hardware.

4. Speed and Agility:

Vast amounts of computing resources can be provisioned in minutes, giving businesses a lot of flexibility and taking the pressure off capacity planning.

5. Security:

Many cloud providers offer a set of policies, technologies, and controls that strengthen your security posture overall, helping protect data, apps, and infrastructure from potential threats.

Challenges of Cloud Computing

1. Downtime:

Service outages can occur, leading to loss of access to data and applications.

2. Security and Privacy:

Storing sensitive information off-premises can pose risks if not properly managed.

3. Compliance:

Businesses need to ensure that their cloud provider is compliant with regulatory requirements.

4. Cost Management:

Managing costs can be difficult as cloud expenses can accumulate rapidly.

5. Vendor Lock-In:

Switching cloud providers can be complicated and costly due to differences in platforms and technologies.

Future Trends in Cloud Computing

1. Edge Computing:

Processing data closer to where it is generated (at the edge) to reduce latency and bandwidth use.

2. AI and Machine Learning:

Increased integration of AI and machine learning capabilities in cloud platforms.

3. Serverless Computing:

Allows developers to build and run applications without having to manage the infrastructure.

4. Multi-Cloud Strategies:

Organizations using multiple cloud services from different providers to avoid vendor lock-in and increase reliability.

5. Quantum Computing:

As quantum computing technology advances, it may be offered through cloud services, providing immense computational power.

Future Trends in Big Data Analytics

1. Artificial Intelligence and Machine Learning:

Increasing use of AI and ML to automate and enhance data analytics.

2. Edge Computing:

Processing data closer to where it is generated to reduce latency and bandwidth usage.

3. Data-as-a-Service (DaaS):

Offering data and analytics services through cloud platforms.

4. Increased Focus on Data Privacy:

Adhering to stricter data protection regulations and ensuring privacy.

5. Real-time Analytics:

Growing demand for real-time data processing and analysis.

Virtual Reality

Virtual Reality (VR) is an immersive technology that uses computer-generated environments to simulate physical presence in real or imagined worlds. This technology is widely used in various fields, from entertainment and education to healthcare and beyond.

Applications of Virtual Reality

1. Entertainment:

Gaming: Immersive video games where players can interact with the environment and characters in a virtual world.

Movies and Virtual Cinemas: Watching 360-degree videos and movies with a sense of presence and immersion.

2. Education and Training:

Simulations: Training for various professions such as pilots, surgeons, and soldiers through realistic simulations.

Virtual Classrooms: Interactive and immersive educational experiences for students in various subjects.

3. Healthcare:

Medical Training: Simulating surgeries and medical procedures for training purposes.

Therapy: Treating conditions like PTSD, phobias, and chronic pain through virtual environments.

4. Real Estate:

Virtual Tours: Allowing potential buyers to explore properties remotely through VR tours.

Architectural Visualization: Viewing and interacting with architectural designs before they are built.

5. Tourism:

Virtual Travel: Exploring tourist destinations and landmarks through immersive VR experiences.

Cultural Heritage: Visiting historical sites and museums virtually.

6. Retail:

Virtual Shopping: Trying on clothes, accessories, and even furniture in a virtual store environment.

Product Visualization: Viewing and interacting with products before purchasing.

7. Work and Collaboration:

Virtual Meetings: Holding meetings and collaborative work sessions in a virtual environment.

Remote Work: Enhancing the remote work experience with virtual offices and workspaces.

Advantages of Virtual Reality

1. Immersive Experience: Provides a sense of presence and immersion that traditional media cannot match.
2. Enhanced Learning and Training: Offers realistic simulations for better understanding and retention of information.
3. Accessibility: Allows users to experience environments and activities that may be otherwise inaccessible.
4. Increased Engagement: Interactive and engaging experiences that can capture users’ attention effectively.

Blockchain

Blockchain is a decentralized, distributed ledger technology. It records transactions across multiple computers in such a way that the registered transactions cannot be altered retroactively. This technology is the backbone of cryptocurrencies like Bitcoin and Ethereum but has applications beyond digital currencies.

Key Characteristics of Blockchain

1. Decentralization:

Unlike traditional databases that are controlled by a central authority, a blockchain is managed by a distributed network of nodes (computers) that work together to validate and record transactions.

2. Transparency:

All transactions are recorded on a public ledger that anyone can view, ensuring transparency and accountability.

3. Immutability:

Once a transaction is recorded on the blockchain, it cannot be altered or deleted. This immutability ensures the integrity of the data.

4. Security:

Blockchain uses cryptographic techniques to secure transactions and control the creation of new units, making it highly resistant to fraud and hacking.

Components of Blockchain

1. Blocks:

Each block contains a list of transactions, a timestamp, a reference to the previous block (creating a chain), and a cryptographic hash of its contents.

2. Nodes:

Computers that participate in the blockchain network by validating and relaying transactions. Each node has a copy of the entire blockchain.

3. Consensus Mechanisms:

Protocols used by the nodes to agree on the validity of transactions and the state of the blockchain. Common mechanisms include Proof of Work (PoW) and Proof of Stake (PoS).

4. Smart Contracts:

Self-executing contracts with the terms of the agreement directly written into code. They automatically enforce and execute the terms of the contract when predefined conditions are met.

Applications of Blockchain

1. Cryptocurrencies:

Bitcoin: The first and most well-known cryptocurrency that introduced the concept of blockchain.

Ethereum: A blockchain platform that enables the creation of smart contracts and decentralized applications (DApps).

2. Supply Chain Management:

Tracking the origin and journey of products to ensure authenticity and reduce fraud.

3. Finance and Banking:

Streamlining cross-border payments, reducing transaction costs, and increasing the speed of transactions.

4. Healthcare:

Securely storing and sharing patient records, ensuring data privacy, and improving data integrity.

5. Voting Systems:

Creating transparent and tamper-proof voting systems to enhance the integrity of elections.

6. Real Estate:

Simplifying property transactions, reducing fraud, and ensuring transparent ownership records.

7. Identity Management:

Providing secure and immutable digital identities for individuals, reducing identity theft.

8. Energy Trading:

Enabling peer-to-peer energy trading and improving the efficiency of energy markets.

Advantages of Blockchain

1. Enhanced Security:

Cryptographic security ensures data integrity and protection against unauthorized access.

2. Increased Transparency:

Public ledgers provide visibility into all transactions, promoting trust and accountability.

3. Reduced Costs:

Eliminates the need for intermediaries, reducing transaction fees and operational costs.

4. Improved Traceability:

Provides a clear and auditable trail of transactions, useful for supply chain management and anti-counterfeiting.

5. Efficiency and Speed:

Automates and streamlines processes, reducing the time required for transactions and settlements.

Challenges of Blockchain

1. Scalability:

Handling a large number of transactions per second remains a challenge for many blockchain networks.

2. Energy Consumption:

Proof of Work (PoW) consensus mechanisms, like those used in Bitcoin, consume significant amounts of energy.

3. Regulation and Compliance:

Navigating the complex regulatory landscape for blockchain and cryptocurrencies can be challenging.

4. Interoperability:

Ensuring different blockchain networks can communicate and work together seamlessly.

5. Privacy Concerns:

Balancing transparency with the need for privacy and confidentiality in certain transactions.

Future Trends in Blockchain

1. Integration with IoT:

Combining blockchain with the Internet of Things (IoT) for secure and transparent data sharing among devices.

2. Development of New Consensus Mechanisms:

Exploring alternatives to Proof of Work (PoW) that are more energy-efficient, such as Proof of Stake (PoS) and Delegated Proof of Stake (DPoS).

3. Blockchain in Government:

Governments exploring the use of blockchain for various applications, including digital identities, land registries, and public services.

4. Advancements in Smart Contracts:

Enhancing the capabilities of smart contracts to handle more complex transactions and automate various business processes.

5. Enterprise Adoption:

Increasing adoption of blockchain technology by large enterprises for supply chain management, finance, and other use cases.

Artificial Intelligence (AI):

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. The processes include learning (acquiring information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

1. Machine Learning (ML):

A subset of AI that involves the development of algorithms that allow computers to learn from and make decisions based on data.

Supervised Learning: The model is trained on a labeled dataset, which means that each training example is paired with an output label.

Unsupervised Learning: The model is given data without explicit instructions on what to do with it and must find patterns and relationships.

Reinforcement Learning: The model learns by receiving rewards or penalties for the actions it performs.

2. Neural Networks:

Inspired by the human brain, these networks consist of interconnected units (neurons) that process information in layers. Deep Learning, a subset of ML, uses deep neural networks with many layers to model complex patterns in data.

3. Natural Language Processing (NLP):

The ability of machines to understand, interpret, and generate human language. Applications include language translation, sentiment analysis, and chatbots.

4. Computer Vision:

Enables machines to interpret and make decisions based on visual data from the world, such as images and videos. Applications include facial recognition, object detection, and autonomous vehicles.

5. Robotics:

Involves designing and creating robots that can perform tasks autonomously or semi-autonomously. This field overlaps with AI when robots are designed to learn from their environments and make decisions.

6. Expert Systems:

AI systems that emulate the decision-making abilities of a human expert. They use a knowledge base and a set of rules to simulate human reasoning.

Applications of Artificial Intelligence

1. Healthcare:

AI is used for diagnosing diseases, personalizing treatment plans, drug discovery, and managing patient records. For example, AI algorithms can analyze medical images to detect anomalies.

2. Finance:

AI algorithms are employed for fraud detection, algorithmic trading, credit scoring, and personalized financial services.

3. Transportation:

Autonomous vehicles, traffic management systems, and predictive maintenance of vehicles all leverage AI to improve efficiency and safety.

4. Retail:

AI powers recommendation systems, inventory management, customer service chatbots, and personalized marketing.

5. Manufacturing:

AI is used for predictive maintenance, quality control, supply chain optimization, and automation of repetitive tasks.

6. Entertainment:

Content recommendation (e.g., Netflix, Spotify), game development, and creating realistic special effects in movies.

7. Education:

Personalized learning experiences, automated grading, and tutoring systems that adapt to the needs of individual students.

8. Customer Service:

AI chatbots and virtual assistants handle customer inquiries, provide information, and improve customer satisfaction.

Advantages of Artificial Intelligence

1. Efficiency and Productivity:

AI can automate repetitive tasks, freeing up human workers to focus on more complex and creative tasks.

2. Data Analysis:

AI can process and analyze large volumes of data quickly, providing insights that would be difficult or impossible for humans to detect.

3. Personalization:

AI systems can tailor experiences and recommendations based on individual user preferences and behavior.

4. Accuracy and Precision:

AI can reduce human error, especially in fields like healthcare and finance where precision is crucial.

5. Continuous Improvement:

Machine learning models can continuously learn and improve over time with more data.

Artificial Intelligence (AI) can be broadly classified into two categories: Weak AI (also known as Narrow AI) and Strong AI (also known as General AI).

Weak AI (Narrow AI)

Weak AI refers to systems designed and trained for a specific task. These systems operate within a limited domain and cannot perform tasks outside of their designated functions.

Characteristics:

1. Specialized: Designed to solve specific problems or perform specific tasks.
2. No Consciousness: Lacks self-awareness, consciousness, or understanding.
3. Limited Scope: Operates under a narrow range of predefined conditions.

Examples:

1. Virtual Assistants: Siri, Alexa, and Google Assistant that perform tasks like setting reminders, answering questions, and controlling smart home devices.
2. Recommendation Systems: Algorithms used by Netflix, Amazon, and Spotify to suggest content based on user preferences.
3. Chatbots: Customer service bots that handle specific types of queries and interactions.
4. Autonomous Vehicles: Self-driving cars that navigate and operate within a specific set of rules and environments.

Strong AI (General AI)

Strong AI refers to systems with the ability to understand, learn, and apply intelligence across a wide range of tasks, mimicking human cognitive functions. These systems possess the potential for general intelligence.

Characteristics:

1. Generalized: Capable of performing any intellectual task that a human can do.
2. Self-Aware: Possesses self-awareness, consciousness, and understanding.
3. Adaptive Learning: Learns and adapts to new situations and tasks without human intervention.

Current Status:

As of now, Strong AI is theoretical and does not yet exist. Researchers are working towards developing such systems, but achieving human-like general intelligence remains a significant challenge.

Potential Applications:

1. Universal Personal Assistants: Assistants capable of understanding and performing a wide range of tasks without specific programming.
2. Healthcare: Diagnosing and treating a vast array of medical conditions with human-like understanding and empathy.
3. Education: Personalized education systems that adapt to the learning needs and styles of individual students.
4. Research: Conducting scientific research and discovering new knowledge across various fields autonomously.

Robotic Process Automation (RPA)

Robotic Process Automation (RPA) refers to the use of software robots or "bots" to automate repetitive, rule-based tasks traditionally performed by human workers.

RPA aims to increase efficiency, reduce errors, and lower operational costs by automating mundane and time-consuming processes.

Components of RPA

1. Software Bots:

These are the virtual workers that perform automated tasks. Bots can be programmed to mimic human interactions with digital systems, such as data entry, processing transactions, and responding to emails.

2. RPA Platforms:

These platforms provide the tools and infrastructure needed to create, deploy, and manage software bots. Leading RPA platforms include UiPath, Blue Prism, and Automation Anywhere.

3. Workflow Designer:

A graphical interface that allows users to design and configure automation workflows without needing deep programming knowledge.

4. Control Room:

A centralized interface to monitor, schedule, and manage the bots, ensuring they are functioning correctly and efficiently.

Applications of RPA

1. Finance and Accounting:

Automating invoice processing, accounts payable/receivable, reconciliation, and financial reporting.

2. Human Resources:

Streamlining employee onboarding/offboarding, payroll processing, and benefits administration.

3. Customer Service:

Handling customer queries, processing orders, and updating customer records.

4. Healthcare:

Automating patient registration, claims processing, and appointment scheduling.

5. Supply Chain Management:

Managing inventory, processing orders, and tracking shipments.

6. IT Services:

Automating routine IT support tasks like password resets, system monitoring, and software updates.

Advantages of RPA

1. Increased Efficiency:

Bots work faster and more accurately than humans, leading to increased productivity and reduced cycle times.

2. Cost Savings:

Reduces labor costs by automating tasks previously performed by human workers.

3. Scalability:

RPA systems can be scaled up or down easily to meet changing business needs.

4. Improved Accuracy:

Eliminates human errors associated with repetitive tasks, leading to higher data quality and reliability.

5. Enhanced Compliance:

Ensures that processes are performed consistently and in accordance with regulatory requirements, aiding in compliance and auditability.

1. Which is meant for IT professionals which will help them to upgrade their skills?  

(a) FutureSkills  

(b) Technology  

(c) Internet of Things  

(d) Processing

Answer: a

2. FutureSkills is a simple yet powerful platform which can go a long way in ……… your career.  

(a) Present proofing  

(b) Past proofing  

(c) Future proofing  

(d) All of these

Answer: c

3. Which of the following objects to be controlled remotely across existing network infrastructure?  

(a) FutureSkills  

(b) IoT  

(c) Cloud computing  

(d) SaaS

Answer: b

4. ...... are key components that help you to collect live data from the surrounding environment.  

(a) Sensors  

(b) Connectivities  

(c) User interfaces  

(d) None of these

Answer: a

5. IoT system provides substantial personal data in ...... detail.  

(a) Minimum  

(b) Maximum  

(c) Medium  

(d) All of these

Answer: b

6. Which of the following is the process of collecting, organizing and analyzing large sets of data to discover patterns and other useful information?  

(a) FutureSkills  

(b) IoT  

(c) Big Data Analytics  

(d) User Interface

Answer: dc

7. ...... of big data refers to structured, unstructured and semi-structured data that is gathered from multiple sources.  

(a) Feature  

(b) Analysis  

(c) Privacy  

(d) Variety

Answer: d

8. Big data is used in .....  

(a) Government  

(b) Healthcare  

(c) Banking  

(d) All of these

Answer: d

9. Which of the following provides computing and storage capacity services to heterogeneous community of end recipients?  

(a) Cloud computing  

(b) Big data  

(c) FutureSkills  

(d) Robotics

Answer: a

10. What is/are characteristics of cloud computing?  

(a) On demand self-services  

(b) Broad network access  

(c) Resource pooling  

(d) All of the above

Answer: d

11. Which type of cloud deployments is used to serve multiple users, not a single customer?  

(a) Private cloud  

(b) Public cloud  

(c) Hybrid cloud  

(d) None of these

Answer: b

12. Which cloud computing services refers to supply on demand environment for developing software applications?  

(a) SaaS  

(b) AaaS  

(c) PaaS  

(d) IaaS

Answer: c

13. Virtual reality is primarily experienced through ...... of the five senses.  

(a) Two  

(b) Three  

(c) Four  

(d) One

Answer: a

14. Which technology devices is used for virtual gaming experiences?  

(a) FutureSkills  

(b) Virtual  

(c) Big data  

(d) Blockchain

Answer: b

15. Virtual reality technology is applied to advance fields of  

(a) medicine  

(b) engineering  

(c) education  

(d) All of these

Answer: d

16. ...... is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.  

(a) Artificial Intelligence  

(b) Introduction of Things  

(c) FutureSkills  

(d) Robotics

Answer: a

17. Which type of artificial intelligence embodies a system designed to carry out one particular job?  

(a) Weak AI  

(b) Strong AI  

(c) Both (a) and (b)  

(d) None of the above

Answer: a

18. AI is important because it can help to solve immensely difficult issues in  

(a) entertainment  

(b) education  

(c) health  

(d) All of these

Answer: d

19. Which of the following originated as a way to interact with friends and family?  

(a) Social Media  

(b) Blockchain  

(c) Automation  

(d) Artificial Intelligence

Answer: a

20. ...... is a social media designed specifically for the business community.  

(a) Pinterest  

(b) Twitter  

(c) LinkedIn  

(d) Facebook

Answer: c

21. Which of the following is an encrypted and distributed database that records data?  

(a) Automation  

(b) Blockchain  

(c) Artificial Intelligence  

(d) FutureSkills

Answer: b

22. Blockchain mechanism brings everyone to the highest degree of ....... .  

(a) Accountability  

(b) Availability  

(c) Performance  

(d) Analytics

Answer: a

23. Additive manufacturing uses…… software.  

(a) System  

(b) Application  

(c) Utility  

(d) CAD

Answer: d

24. Example(s) of 3D printing is/are  

(a) Eyewear  

(b) Dental products  

(c) Architectural scale model  

(d) All of the above

Answer: d

25. RPA is the use of specialized computer programs known as ......  

(a) FutureSkills  

(b) User Interface  

(c) Software Robot  

(d) Artificial Intelligence

Answer: c

26. What is/are business benefit(s) of RPA?  

(a) Increased accuracy  

(b) No interruption of work  

(c) Low technical barriers  

(d) All of the above

Answer: d

27. ...... can help companies offer better customer service by automating contact center tasks.  

(a) RPA  

(b) Interface  

(c) Additive  

(d) Blockchain

Answer: a

28. Which of the following involves protecting information and systems from major cyber threats?  

(a) Cyber crime  

(b) Cyber security  

(c) Robotics  

(d) Virtual reality

Answer: b

29. It is the guarantee of reliable and constant access to your sensitive data by authorized people.  

(a) Confidentiality  

(b) Integrity  

(c) Availability  

(d) None of these

Answer: c

30. ...... creates a barrier between the computer and any unauthorized program trying to come in through the Internet.  

(a) Firewall  

(b) Integrity  

(c) Blockchain  

(d) Automation

Answer: a

31. There are security apps available from all known companies such as  

(a) Avast  

(b) McAfee  

(c) Norton  

(d) All of these

Answer: d

32. It is a software that helps to protect the computer from any unauthorized code or software that creates a threat to the system.  

(a) Antivirus  

(b) Password  

(c) Passcode  

(d) Firewall

Answer: a

33. FutureSkills platform will ensure that all the needs are fulfilled. that creates a threat to the system.  

(a) True  

(b) False   

Answer: a

34. The goal of IoT is to extend the Internet connectivity from standard devices like computer.  

(a) True  

(b) False   

Answer: a

35. Some data is sent to a cloud infrastructure.  

(a) True  

(b) False   

Answer: b

36. IoT technology does not help a lot in improving technologies and making them better.  

(a) True  

(b) False   

Answer: b

37. IoT offers real time information leading to effective decision-making. improving technologies and making them better.  

(a) True  

(b) False   

Answer: a

38. In big data, volume refers to the speed at which data is being created in real time. improving technologies and making them better.  

(a) True  

(b) False   

Answer: b

39. The banking sector relies on big data for fraud detection. improving technologies and making them better.  

(a) True  

(b) False   

Answer: a

40. Hybrid cloud is combination of private cloud and public cloud.  

(a) True  

(b) False   

Answer: a

41. Cloud computing could bring hardware costs down.  

(a) True  

(b) False   

Answer: a

42. Artificial Intelligence is a computer interface which tries to mimic real world beyond the flat monitor.  

(a) True  

(b) False   

Answer: b

43. Virtual reality has also been adopted in business.  

(a) True  

(b) False   

Answer: a

44. Knowledge engineering is a core part of AI research.  

(a) True  

(b) False   

Answer: a

45. Pinterest is a free microblogging service.  

(a) True  

(b) False   

Answer: b

46. Blockchain technology does not provide durability, reliability and longevity with decentralized network  

(a) True  

(b) False   

Answer: b

. 47. Additive manufacturing adds material to create an object.  

(a) True  

(b) False   

Answer: a

48. Programming skills are not necessary to configure a software robot.  

(a) True  

(b) False   

Answer: a

49. Confidentiality refers to protecting information from being accessed by unauthorized parties.  

(a) True  

(b) False   

Answer: a

50. Complex passwords are simple for the hackers to find.  

(a) True  

(b) False   

Answer: b