In AI & Machine Learning, which option best describes Artificial intelligence?

The field of creating systems that perform tasks associated with intelligence

Grouping similar examples without predefined labels

One complete pass through a training dataset

A setting controlling the step size of optimization updates

In AI & Machine Learning, which option best describes Machine learning?

A method in which systems learn patterns from data

A model made of interconnected computational units or layers

A function measuring how far predictions are from desired outcomes

A model setting selected rather than directly learned from training data

In AI & Machine Learning, which option best describes Supervised learning?

Machine learning using neural networks with multiple layers

An optimization method using gradients to update model parameters

The fraction of predicted positives that are actually positive

In AI & Machine Learning, which option best describes Unsupervised learning?

Learning patterns from unlabeled data

One complete pass through a training dataset

A setting controlling the step size of optimization updates

The fraction of actual positives correctly identified

In AI & Machine Learning, which option best describes Reinforcement learning?

Learning through interactions and rewards or penalties

A function measuring how far predictions are from desired outcomes

A model setting selected rather than directly learned from training data

The proportion of predictions that are correct

In AI & Machine Learning, which option best describes Feature?

An input variable used by a machine-learning model

An optimization method using gradients to update model parameters

The fraction of predicted positives that are actually positive

The harmonic mean of precision and recall

In AI & Machine Learning, which option best describes Label?

The target value in a supervised-learning dataset

A setting controlling the step size of optimization updates

The fraction of actual positives correctly identified

A table summarizing classification predictions by actual and predicted class

In AI & Machine Learning, which option best describes Training data?

Data used to fit a machine-learning model

A model setting selected rather than directly learned from training data

The proportion of predictions that are correct

A function applied to a neural network unit's input

In AI & Machine Learning, which option best describes Training set?

The portion of data used to train a model

The fraction of predicted positives that are actually positive

The harmonic mean of precision and recall

An activation function commonly defined as max(0,x)

In AI & Machine Learning, which option best describes Validation set?

Data used to tune or select model settings during development

The fraction of actual positives correctly identified

A table summarizing classification predictions by actual and predicted class

A method for computing gradients through a neural network

In AI & Machine Learning, which option best describes Test set?

Held-out data used to evaluate a model

The proportion of predictions that are correct

A function applied to a neural network unit's input

A neural network architecture especially suited to spatial patterns such as images

In AI & Machine Learning, which option best describes Model?

A learned or defined representation used for prediction or decision-making

The harmonic mean of precision and recall

An activation function commonly defined as max(0,x)

A neural network architecture designed for sequential data

Easy Free Computer & Technology AI & Machine Learning

Cloud Computing Quiz

Review cloud computing concepts and the services that make modern infrastructure scalable.

25 Questions

30 min Duration

40% To pass

1.00 Marks / correct

Instructions

This quiz has 25 questions.
Questions are picked at random from a bank of 50, so each attempt is different.
You have 30 minutes. The quiz submits automatically when time runs out.
You need 40% to pass.
There is no negative marking.
You can mark questions for review and return to them before submitting.
Correct answers and explanations are shown after you submit.

Sample Questions & Answers

A preview of 12 questions from this quiz, each with the correct answer and a written explanation. Attempt the full, timed quiz to be scored and earn XP.

Question 1
In AI & Machine Learning, which option best describes Artificial intelligence?
- Grouping similar examples without predefined labels
- The field of creating systems that perform tasks associated with intelligence Correct answer
- One complete pass through a training dataset
- A setting controlling the step size of optimization updates
Explanation: Artificial intelligence is correctly described by: The field of creating systems that perform tasks associated with intelligence.
Question 2
In AI & Machine Learning, which option best describes Machine learning?
- A method in which systems learn patterns from data Correct answer
- A model made of interconnected computational units or layers
- A function measuring how far predictions are from desired outcomes
- A model setting selected rather than directly learned from training data
Explanation: Machine learning is correctly described by: A method in which systems learn patterns from data.
Question 3
In AI & Machine Learning, which option best describes Supervised learning?
- Machine learning using neural networks with multiple layers
- An optimization method using gradients to update model parameters
- The fraction of predicted positives that are actually positive
- Learning from labeled examples Correct answer
Explanation: Supervised learning is correctly described by: Learning from labeled examples.
Question 4
In AI & Machine Learning, which option best describes Unsupervised learning?
- One complete pass through a training dataset
- A setting controlling the step size of optimization updates
- Learning patterns from unlabeled data Correct answer
- The fraction of actual positives correctly identified
Explanation: Unsupervised learning is correctly described by: Learning patterns from unlabeled data.
Question 5
In AI & Machine Learning, which option best describes Reinforcement learning?
- A function measuring how far predictions are from desired outcomes
- Learning through interactions and rewards or penalties Correct answer
- A model setting selected rather than directly learned from training data
- The proportion of predictions that are correct
Explanation: Reinforcement learning is correctly described by: Learning through interactions and rewards or penalties.
Question 6
In AI & Machine Learning, which option best describes Feature?
- An input variable used by a machine-learning model Correct answer
- An optimization method using gradients to update model parameters
- The fraction of predicted positives that are actually positive
- The harmonic mean of precision and recall
Explanation: Feature is correctly described by: An input variable used by a machine-learning model.
Question 7
In AI & Machine Learning, which option best describes Label?
- A setting controlling the step size of optimization updates
- The fraction of actual positives correctly identified
- A table summarizing classification predictions by actual and predicted class
- The target value in a supervised-learning dataset Correct answer
Explanation: Label is correctly described by: The target value in a supervised-learning dataset.
Question 8
In AI & Machine Learning, which option best describes Training data?
- A model setting selected rather than directly learned from training data
- The proportion of predictions that are correct
- Data used to fit a machine-learning model Correct answer
- A function applied to a neural network unit's input
Explanation: Training data is correctly described by: Data used to fit a machine-learning model.
Question 9
In AI & Machine Learning, which option best describes Training set?
- The fraction of predicted positives that are actually positive
- The portion of data used to train a model Correct answer
- The harmonic mean of precision and recall
- An activation function commonly defined as max(0,x)
Explanation: Training set is correctly described by: The portion of data used to train a model.
Question 10
In AI & Machine Learning, which option best describes Validation set?
- Data used to tune or select model settings during development Correct answer
- The fraction of actual positives correctly identified
- A table summarizing classification predictions by actual and predicted class
- A method for computing gradients through a neural network
Explanation: Validation set is correctly described by: Data used to tune or select model settings during development.
Question 11
In AI & Machine Learning, which option best describes Test set?
- The proportion of predictions that are correct
- A function applied to a neural network unit's input
- A neural network architecture especially suited to spatial patterns such as images
- Held-out data used to evaluate a model Correct answer
Explanation: Test set is correctly described by: Held-out data used to evaluate a model.
Question 12
In AI & Machine Learning, which option best describes Model?
- The harmonic mean of precision and recall
- An activation function commonly defined as max(0,x)
- A learned or defined representation used for prediction or decision-making Correct answer
- A neural network architecture designed for sequential data
Explanation: Model is correctly described by: A learned or defined representation used for prediction or decision-making.