This guide prepares you for the version of Introduction to Emerging Technologies indexed as an “Emerging Technologies Final Exam” document. That copy is associated with Select Business and Technology College, course EmTe 1011/1012, and is dated October 2021; it lists a 1-hour-30-minute examination covering artificial intelligence, machine learning, the Internet of Things, sensors, mixed reality, digital privacy, ethics and technology risks. Other institutions use the same or similar title, so confirm your institution, course code, instructor’s terminology, exam date and marking scheme before relying on any exact format. The indexed page is a study document, not proof of a current official paper or answer key: view the indexed copy.
What the exam guide covers
Likely question areas are:
- What “emerging technology” means and how it differs from an invention, mature technology and disruptive technology
- Artificial intelligence (AI), machine learning, deep learning and generative AI
- AI capability and functionality categories
- Internet of Things (IoT), sensors, actuators, networks and IoT architecture
- AI combined with IoT (AIoT)
- Virtual reality (VR), augmented reality (AR) and mixed reality (MR)
- Digital privacy, ethics, cybersecurity and social risks
- Cloud computing and related communication technologies
“Emerging” is relative. Cloud computing, IoT and machine learning are established technologies in 2026, although their applications and capabilities continue to develop. Biotechnology, robotics, quantum computing, blockchain, big-data systems and extended reality may be emerging in one industry or region while already mature in another.
Artificial intelligence explained
NIST defines AI as a machine-based system that, for human-defined objectives, can produce predictions, recommendations or decisions that influence real or virtual environments: NIST AI definition. AI is a broad field, not a synonym for robots or human consciousness.
What AI systems do
- Recognize patterns in text, images, sound or sensor readings
- Classify objects, messages, risks or medical images
- Predict events such as demand, equipment failure or traffic
- Understand or generate language
- Recommend content, products or actions
- Perceive environments and support decisions or automation
An effective model can perform a task without thinking, feeling, possessing common sense or understanding the world as a person does.
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AI, machine learning, deep learning and generative AI
| Term | Meaning | Example |
|---|---|---|
| Artificial intelligence | The broad field of systems that perform capabilities associated with intelligent behavior. | A system that recommends a treatment or controls a delivery robot. |
| Machine learning | An AI approach in which algorithms learn patterns from data and improve with experience. | Fraud detection trained on labelled transactions. |
| Deep learning | Machine learning using multilayer neural networks, often for complex unstructured data. | Speech recognition from audio waveforms. |
| Generative AI | Models that produce new text, images, audio, video or code from learned patterns. | A model drafting a report or creating an image. |
| AI agent | A system that receives inputs, reasons or plans, and takes actions toward a goal. | A service agent that checks a booking and changes it after approval. |
AI categories by capability
- Narrow (weak) AI: built for a specified task or limited class of tasks. Nearly all deployed AI is narrow AI.
- Artificial general intelligence (AGI): a hypothetical system with broad, human-level competence across many domains.
- Superintelligence: a hypothetical system exceeding human performance across substantially all intellectual tasks.
AI categories by functionality
- Reactive machines: respond to current input without a meaningful retained history.
- Limited-memory systems: use recent or historical data to improve an output or decision.
- Theory-of-mind AI: a theoretical category involving understanding other agents’ mental states.
- Self-aware AI: a hypothetical category involving consciousness or self-awareness.
Do not describe AGI, superintelligence, theory-of-mind AI or self-aware AI as ordinary currently deployed products.
Machine learning and deep learning
Four common learning types
- Supervised learning: learns from examples with known labels, such as “spam” and “not spam.”
- Unsupervised learning: finds structure in unlabelled data, such as customer clusters.
- Semi-supervised learning: combines a smaller labelled set with a larger unlabelled set.
- Reinforcement learning: learns actions through feedback and reward signals.
A practical model workflow
- Define the task, users, constraints and success measure.
- Collect, clean and document representative data.
- Split data into training, validation and test sets.
- Train the model and tune it using the validation data.
- Evaluate accuracy and relevant subgroup, safety and robustness measures on held-out data.
- Deploy with access controls, monitoring and a way to report failures.
- Retrain, update or retire it when data, users or requirements change.
High test accuracy does not prove fairness, causation, safety or performance in a new environment. Biased, incomplete or stale data can produce unreliable results; correlation alone does not establish cause. Models can also drift as real-world conditions change.
Internet of Things (IoT) explained
For technical accuracy, IoT is a system of connected physical objects that sense, process, exchange or act on data. NIST emphasizes at least one physical-world transducer (a sensor or actuator) and at least one network interface; interfaces can include Ethernet, Wi-Fi, Bluetooth, LTE, Zigbee or Ultra-Wideband: NIST IoT FAQ. The classroom phrase “any device with an on/off switch connected to the Internet” is an oversimplification.
Examples include thermostats, wearables, industrial machines, connected vehicles, lighting, farm sensors, cameras, medical monitors and smart appliances. A cloud-only software service with no physical sensing or actuation is not necessarily an IoT device.
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Sensors and actuators
- Sensor: measures or detects a condition and produces data.
- Actuator: receives a signal or command and changes something in the physical world, such as opening a valve or switching a motor.
| Classification | Measures or detects | Example |
|---|---|---|
| Environmental | Temperature, humidity, air quality, light or pressure | Greenhouse humidity sensor |
| Motion | Movement, vibration or acceleration | Security-light motion detector |
| Position | Location, orientation, proximity or displacement | Robot-arm position sensor |
| Magnetic | Magnetic field or switch state | Door-open contact |
| Optical | Light or images | Camera or light meter |
| Chemical or biological | Chemical composition or biological signals | Air-quality or heart-rate sensor |
These labels are not universal: one sensor can fit several categories depending on whether classification is by physical quantity, technology or application.
IoT architecture
Textbooks use different numbers and names of layers. The following four-layer model captures the functions tested by the indexed exam; use your instructor’s labels when answering.
| Layer | Function | Typical components or examples |
|---|---|---|
| Sensing or perception | Detects physical conditions and gathers data; may also include local actuators. | Temperature, motion, camera, RFID, GPS and pressure sensors |
| Network or transport | Moves data between devices, gateways, processing systems and applications. | Ethernet, Wi-Fi, Bluetooth, Zigbee, cellular, LPWAN and IP networks |
| Processing, middleware or data | Stores, filters, aggregates and analyses data; coordinates devices and applies rules or models. | Device processor, edge gateway, data centre or cloud platform |
| Application | Presents information, triggers actions and delivers a user-facing service. | Smart-home dashboard, fleet management or patient-monitoring application |
Processing need not occur only in the cloud. A safety-critical controller may act on the device or at an edge gateway to reduce delay, while long-term analytics may run in a cloud platform.
AI and IoT together: AIoT
IoT supplies observations from the physical world; AI can turn those observations into predictions, classifications, recommendations or automated responses. NIST describes the relationship as complementary in its IoT advisory report: NIST IoT and AI report.
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- Vibration sensors can support predictive maintenance.
- Traffic systems can adapt signals to congestion.
- Building controls can optimize energy use.
- Wearables can identify health trends for a clinician to review.
- A farm system can adjust irrigation using soil and weather data.
AIoT can fail when sensors are inaccurate, networks are unavailable, models drift, devices are compromised or automation acts without appropriate human oversight.
Virtual, augmented and mixed reality
| Technology | What the user experiences | Typical example |
|---|---|---|
| Virtual reality (VR) | A predominantly simulated environment replaces the user’s view of the physical world. | Immersive training simulation with a headset |
| Augmented reality (AR) | Digital information is overlaid on the user’s view of the physical world. | Navigation arrows displayed through a phone camera |
| Mixed reality (MR) | Physical and digital elements are combined so virtual objects may be spatially anchored and interact with the environment. | A virtual machine model fixed to a real workbench |
Terminology varies among textbooks and vendors; “mixed reality” is sometimes used broadly within the extended-reality spectrum. Answer according to the definitions taught in your course while preserving the distinctions above.
Digital privacy
Three privacy dimensions
- Information privacy: control over collection, use, retention and disclosure of personal information.
- Communication privacy: protection of messages and calls from unauthorised access or surveillance.
- Individual privacy: personal space, autonomy and freedom from unwanted intrusion.
Principles that are often tested
- Transparency: explain what is collected, why and with whom it is shared.
- Data minimization: collect only what is necessary.
- Purpose limitation: do not reuse data for an unrelated purpose without a valid basis or authorization.
- Accuracy: keep information correct and allow errors to be addressed.
- Security: protect data against unauthorized access, alteration or loss.
- User control: provide meaningful choices, access and deletion where applicable.
- Retention limits: keep data no longer than needed.
- Accountability: assign responsibility and document compliance.
Data minimization and purpose limitation are different: the first concerns how much data is collected; the second concerns what later use is permitted. IoT devices can create special exposure because they may continuously observe homes, workplaces, movements or health conditions. NIST IoT guidance addresses data protection, interface access, secure updates and device security: IoT cybersecurity capabilities.
Ethics and responsible technology
Common principles include honesty, avoiding harm, privacy, fairness, non-discrimination, accountability, transparency and human oversight. An ethical principle states the concern; a control is a practical response.
| Ethical concern | Possible response |
|---|---|
| Privacy | Data minimization, encryption, access control and clear notices |
| Bias or discrimination | Representative data, subgroup testing and ongoing monitoring |
| Safety | Testing, fail-safe design, human override and incident response |
| Accountability | Named owners, documentation, audit trails and appeal routes |
| Transparency | Plain-language explanations and model documentation |
| Security | Secure development, authentication, vulnerability handling and updates |
NIST’s voluntary AI Risk Management Framework organizes risk work into Govern, Map, Measure and Manage: AI RMF functions. Its 1.0 publication is available at NIST AI RMF 1.0 PDF.
Risks of emerging technologies
Risks across technologies
- Cyberattacks, data breaches and identity theft
- Unauthorised surveillance and privacy loss
- Algorithmic bias, discrimination and opaque decisions
- Safety failures and overdependence on automation
- Incorrect, fabricated or misleading AI output
- Job displacement and unequal access (digital exclusion)
- Environmental costs, vendor lock-in and poor interoperability
- Regulatory uncertainty and malicious misuse
IoT-specific failure modes
- Weak default passwords or authentication
- Exposed interfaces and insecure communications
- Unpatched firmware and unsupported end-of-life devices
- Compromise of one device as a route into a larger network
- Unsafe or unauthenticated software updates
NIST’s IoT catalog includes restricted interface access, secure software updates, cybersecurity-state awareness and hardware and software integrity protection: technical capability catalog.
AI-specific failure modes
- Biased or unrepresentative training data
- Hallucinated or incorrect outputs
- Privacy leakage and adversarial manipulation
- Model drift, weak explainability and unclear responsibility
- Overreliance by users or unsafe actions by connected systems
NIST notes that AI risks can affect people, groups, organizations, communities, society and the environment, with probability, duration, scale and impact varying by context: AI RMF 1.0.
Common exam traps and how to handle ambiguity
- Read “not,” “except,” “least” and other negative words twice.
- Use your lecture-note terminology when a question defines a term differently, but choose the technically more complete answer when no course definition is supplied.
- Do not confuse a sensor (collects measurements) with an actuator (acts on the physical world).
- Do not treat AI as identical to machine learning, or cloud computing as merely online storage.
- Do not treat every Internet-connected software service as IoT.
- Do not present general AI or self-aware AI as existing products.
- For an essay, give a definition, explain operation, provide examples, state benefits, identify limitations and recommend safeguards.
Original practice questions
Multiple choice
-
Which description best matches NIST’s AI definition?
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- B. A machine-based system producing predictions, recommendations or decisions for human-defined objectives
- C. A database containing personal information
- D. A device connected to Wi-Fi
Answer: B. A motor, database or network connection alone does not define AI.
-
Which is supervised learning?
- A. Finding clusters without labels
- B. Learning from labelled examples
- C. Acting randomly without feedback
- D. Encrypting a data set
Answer: B.
-
Which IoT layer moves data between a sensor and a processing service?
Answer: The network or transport layer.
-
Which component changes the physical world after receiving a command?
Answer: An actuator.
-
Which is data minimization?
Answer: Collecting only the information necessary for the stated task.
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Which reality technology overlays digital information on the physical scene?
Answer: Augmented reality.
-
Which category is hypothetical?
Answer: Self-aware AI (as well as AGI, superintelligence and theory-of-mind AI).
-
Which is an IoT security weakness?
Answer: Unpatched firmware on an unsupported device.
-
What does AIoT describe?
Answer: AI analysing IoT data to produce insights or responses.
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What is a test-set result unable to guarantee by itself?
Answer: Fairness, causation and safe performance in every deployment environment.
True or false
-
All IoT devices are simply any objects with an Internet connection. False. Physical sensing or actuation is an important part of the technical definition.
-
Deep learning is a form of machine learning. True.
-
Data minimization and purpose limitation mean the same thing. False. They concern collection volume and later use respectively.
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General AI is an ordinary description of most deployed systems. False. Deployed systems are generally narrow AI.
-
An actuator measures temperature. False. A temperature sensor measures; an actuator performs an action.
Short-answer prompts
- Define IoT and explain the roles of a sensor, network interface and actuator.
- List three sensor classifications and give one example of each.
- Explain two ethical principles and a practical safeguard for each.
- Compare VR, AR and MR using one example for each.
- Explain how AI can improve an IoT application and identify two possible failure modes.
Essay prompts
- Describe a smart-building IoT system from sensing layer to application layer. Discuss benefits, privacy and security risks, and safeguards.
- Compare narrow AI, machine learning, deep learning and generative AI. Explain why accuracy alone is not enough to establish a trustworthy system.
Final revision checklist
- Can you define emerging technology and explain why “emerging” depends on time and context?
- Can you distinguish AI, machine learning, deep learning, generative AI and AI agents?
- Can you identify narrow AI and label AGI, superintelligence, theory-of-mind and self-aware AI as hypothetical?
- Can you explain supervised, unsupervised, semi-supervised and reinforcement learning?
- Can you draw the sensing, network, processing and application layers of IoT?
- Can you distinguish sensors from actuators and give examples of sensor categories?
- Can you explain AIoT and name one benefit and one limitation?
- Can you distinguish VR, AR and MR?
- Can you separate data minimization from purpose limitation?
- Can you name ethical principles and matching technical controls?
- Can you identify weak passwords, insecure updates, exposed interfaces and unsupported devices as IoT risks?
- Can you structure an essay as definition, operation, examples, benefits, limitations and safeguards?
Version and source note
The closest exact-title document currently indexed is the three-page Introduction to Emerging Technologies Final Exam associated with Select Business and Technology College and dated October 2021: Scribd listing. Similar titles refer to different courses and institutions, including documents at Scribd and Course Hero. Verify your current syllabus and instructor’s instructions; do not assume the indexed questions, duration or explanations are current or official.
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