AI Training Exam | WE
AI is incorporated into a variety of different types of technology. Here are some of the examples EXCEPT
Automation
Siri
Machine vision
NLP
Which one of these is not an area of AI?
Computer vision/image recognition
Voice recognition
Web design
Robotics
Which of these does NOT use machine learning/AI?
Driverless cars
SIRI/Alexa
Sonos wireless speakers
Facial recognition on your phone
Artificial Intelligence is the process that allows computers to learn and make decisions like humans
TRUE
FALSE
What are two characteristics of artificial intelligence?
It is an agile, responsive network technology that scales easily and adapts to meet business requirements.
It uses systems that mimic human cognitive functions such as learning and problem solving.
It uses intelligent agents that interact with the environment and make decisions to reach a specific goals.
It translates intent into policies and then uses automation to deploy appropriate network configurations.
Artificial intelligence uses statistical techniques to give computers the ability to learn from their environment.
Expert Systems are better than humans and designed to replace them in the future.
TRUE
FALSE
What is the name of the computer program that contains the distilled knowledge of an expert ?
Management information System
Expert system
Data base management system
Artificial intelligence
( Weak AI ) is
A set of computer programs that produce output that would be considered to reflect intelligence if it were generated by humans
The study of mental faculties through the use of mental models implemented on a computer.
The embodiment of human intellectual capabilities within a computer.
All of the above
Which of the following terms best describes AI ?
Probabilistic
Empathetic
Materialistic
Deterministic
Which of the Following is not a learning technique ?
Programmed Learning
Supervised Learning
Reinforcement Learning
Unsupervised Learning
A Deep Neural Network is a network that contains more than one...
Hidden Layer
Output Layer
Hidden Node
Input Layer
Which of the Following is an attribute of strong or Generalized AI ?
Perform independent tasks
Cannot teach itself new strategies
Operate with Human-level Consciousness
Can Perform Specific tasks , but cannot learn new ones
Which of these statements is True ?
Cognitive systems can only translate small volumes of audio data into their literal text translations at massive speeds .
Cognitive systems can learn from their successes and failures
Cognitive systems can derive mathematically precise answers following a rigid decision tree approach
Cognitive systems can only process neatly organized structured data
When Creating Deep Learning algorithms , developers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next .
TRUE
FALSE
The most suitable activation function for hidden layer
Sigmoid
ReLu
Softmax
Tanh
Deep learning algorithm for sequential problem
CNN
ANN
DBN
RNN
The number of batches needed to complete one epoch
Iterations
Epochs
Batch
The total number of training examples present in a single batch
Epochs
Batch
Iteration
How many input layers have the following Neural network ?
1
2
3
4
How many hidden layers have the following Neural network ?
4
1
2
3
When an ENTIRE dataset is passed forward and backward through the neural network only ONCE
One epoch
One batch
One iteration
What is a cost function?
function describes how computationally expensive is a neural network
An algorithm used to find the minimum of a function
Different name for activation function
a way to determine how well the machine learning model has performed given the different values of each parameter
What is activation function?
A way to determine how well the machine learning model has performed given the different values of each parameter
an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost)
Function describes how computationally expensive is a neural network
Function used to enable Neural Network to solve non-linear problems
What if we would like to have prediction output (binary classification) represented by probability, which activation function is the best choice?
Tanh
ReLu
Sigmoid
Linear
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