Artificial Intelligence Essentials (AI|E)

Last checked October 22, 2026 against the official EC-Council exam page.

1. Question 1Building Blocks of AI

   Which component of a neural network applies a non-linear transformation to a neuron's weighted sum of inputs?

   1. AThe dataset split, which divides the examples into training, validation, and test sets before the model is trained
   2. BThe loss function, which measures how far the network's predictions are from the expected results during training
   3. CThe optimizer, which adjusts the weights of the network using gradients to reduce the loss over many training steps
   4. DThe activation function, which transforms the weighted sum so that the network can model non-linear relationships
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   Correct answer: D

   The activation function applies the non-linear transformation that lets networks represent complex relationships. The loss function, optimizer, and dataset split each play different roles in training rather than transforming the neuron's output.

2. Question 2AI Ethics and Responsible AI

   Which principle is central to responsible AI?

   1. ARemoving human oversight entirely, so that the system can make every decision automatically and without delay
   2. BMaximizing secrecy, so that the way the system reaches its decisions is never disclosed to anyone outside the team
   3. CIgnoring the risks of the system, so that the project can move forward without being slowed down by reviews
   4. DFairness, meaning the system should not produce unjustified disparate outcomes for different groups of people
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   Correct answer: D

   Fairness is a core principle of responsible AI, because systems can otherwise treat groups unjustly. Secrecy, ignoring risk, and removing oversight each work against accountability and the protection of people affected by the system.

3. Question 3Introduction to Artificial Intelligence

   Which pair of terms is described correctly?

   1. AData is the examples used for learning, and a model is the learned function that maps inputs to predictions
   2. BData is a computer and a model is a monitor that displays the output of the computer for its users
   3. CData is the program code of an application, and a model is the written documentation that describes that code
   4. DData and a model are two names for the same thing, so either term can be used when describing an AI system
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   Correct answer: A

   Data is the set of examples that a system learns from, while a model is the function it learns and then uses to predict. The other options confuse hardware, code, and documentation with the data and the learned model.

4. Question 4Everyday AI Tools and Use Cases

   Which everyday application is a typical example of a recommendation system?

   1. AA thermometer that measures the temperature of a room and displays the reading on a small digital screen
   2. BA basic calculator that performs arithmetic on the numbers that a user types in at the keypad of the device
   3. CA paper clip that holds sheets of documents together so that they can be filed in the correct folder later
   4. DA streaming service that suggests films to watch based on a viewer's previous viewing history and ratings
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   Correct answer: D

   Recommendation systems predict what a user is likely to want based on past behaviour, which is what the streaming example does. The calculator, paper clip, and thermometer perform fixed functions without learning from user behaviour.

5. Question 5Prompt Crafting for Effective AI Interactions

   What is a prompt in generative AI?

   1. AA printed label that is attached to a server rack so that the operations team can identify the machine quickly
   2. BThe input instruction or context that is given to a model to guide the content and format of its output
   3. CA password that the user enters to sign in to the AI tool at the start of each session in the browser
   4. DA driver file that the graphics card uses to communicate with the operating system of the computer host
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   Correct answer: B

   A prompt is the instruction and context that steer a generative model's response. Labels, passwords, and drivers are unrelated objects that do not guide the model's output.