Question 1

What is the purpose of evaluation in the AI project cycle?

Accepted Answer

To assess the performance and impact of the AI system.

Answer

To generate ideas for the AI system.

Answer

To determine the hardware requirements of the AI system.

Answer

To design the user interface of the AI system.

Question 2

What is the main purpose of evaluating data in the AI project cycle?

Accepted Answer

To ensure the quality and suitability of the data for training the AI model.

Answer

To evaluate the performance of the AI system.

Answer

To determine the cost of the AI project.

Answer

To design the user interface of the AI system.

Answer

To select the programming language for the AI project.

Question 3

What does F1 Score measure in AI?

Accepted Answer

The balance between precision and recall.

Answer

The amount of data processed by AI.

Answer

The accuracy of AI models.

Answer

The complexity of AI architectures.

Answer

The speed of AI algorithms.

Question 4

Which value can the F1 Score range from?

Accepted Answer

0 to 1

Answer

0 to infinity

Answer

-1 to 1

Answer

1 to 10

Answer

0 to 100

Question 5

How is the F1 Score calculated in AI?

Accepted Answer

2 * (precision * recall) / (precision + recall)

Answer

2 * (recall - precision) / (precision + recall)

Answer

precision * recall

Answer

(precision - recall) / (precision + recall)

Answer

(precision + recall) / 2

Question 6

What is the formula for calculating Precision in AI?

Accepted Answer

True Positives / (True Positives + False Positives)

Answer

True Negatives / (True Negatives + False Negatives)

Answer

False Negatives / (False Negatives + True Negatives)

Answer

False Positives / (False Positives + True Positives)

Answer

True Positives / (True Positives + False Negatives)

Question 7

In AI, what does the F1 Score consider?

Accepted Answer

Both precision and recall.

Answer

Recall and specificity.

Answer

Precision and error rate.

Answer

Sensitivity and specificity.

Answer

Accuracy and recall.

Question 8

What is Precision in evaluation metrics?

Accepted Answer

A measure of the accuracy of positive predictions.

Answer

A measure of the accuracy of both positive and negative predictions.

Answer

A measure of the accuracy of negative predictions.

Answer

A measure of the total number of predictions made.

Question 9

What is Accuracy in evaluation metrics?

Accepted Answer

The proportion of correctly classified instances.

Answer

The ratio of true negative predictions to the sum of true negative and false negative predictions.

Answer

The proportion of incorrectly classified instances.

Answer

The ratio of true positive predictions to the sum of true positive and false positive predictions.

Question 10

What is the purpose of the testing phase in an AI project cycle?

Accepted Answer

To evaluate the performance and accuracy of the AI model.

Answer

To collect data for training the AI model.

Answer

To deploy the AI model into production.

Answer

To design the user interface of the AI application.

Question 11

What does the 'W' stand for in the 4W CANVAS framework?

Accepted Answer

Who

Answer

When

Answer

What

Answer

Where

Question 12

What does the 'Why' element represent in the 4W Canvas in AI?

Accepted Answer

Purpose or Objective

Answer

Timeframe

Answer

Context or Environment

Answer

Parties Involved

Question 13

What does the 'Who' element represent in the 4W Canvas in AI?

Accepted Answer

Parties Involved

Answer

Timeframe

Answer

Purpose or Objective

Answer

Context or Environment

Question 14

What does the 'When' element represent in the 4W Canvas in AI?

Accepted Answer

Timeframe

Answer

Purpose or Objective

Answer

Parties Involved

Answer

Context or Environment

Question 15

What does the 'Canvas' refer to in the 4W Canvas in AI?

Accepted Answer

Framework or Template

Answer

Machine Learning Model

Answer

Programming Language

Answer

Visualization Tool

Question 16

Which element of the 4W Canvas in AI focuses on the individuals, organizations, or entities involved in the AI project?

Accepted Answer

Who

Answer

When

Answer

Where

Answer

What

Question 17

What is the purpose of data exploration in the AI project cycle?

Accepted Answer

To gain insights and understand patterns in the data.

Answer

To create AI models and algorithms.

Answer

To deploy AI models in production.

Answer

To collect and label data.

Question 18

What is a common technique used in data exploration?

Accepted Answer

Data visualization

Answer

Data deployment

Answer

Data labeling

Answer

Data collection

Question 19

What is the main goal of data preprocessing in the AI project cycle?

Accepted Answer

To clean and prepare the data for analysis.

Answer

To generate synthetic data for analysis.

Answer

To build AI models and algorithms.

Answer

To label the data for training.

Question 20

What is data cleaning in AI?

Accepted Answer

Process of identifying and correcting or removing errors, inaccuracies, and inconsistencies in data.

Answer

Process of creating models from data.

Answer

Process of analyzing patterns in data.

Answer

Process of visualizing data.

Answer

Process of scaling and transforming data.

Question 21

Which of the following is a common data cleaning task in AI?

Accepted Answer

Handling missing data.

Answer

Increasing data complexity.

Answer

Changing data encoding.

Answer

Applying dimensionality reduction.

Answer

Randomly shuffling the dataset.

Question 22

What is problem scoping in AI?

Accepted Answer

Defining the specific problem and its boundaries

Answer

Evaluating the AI model

Answer

Training the AI model

Answer

Collecting and cleaning data

Question 23

Why is problem scoping important in AI?

Accepted Answer

To ensure focused and relevant results

Answer

To train the AI model effectively

Answer

To evaluate the accuracy of the AI model

Answer

To collect large amounts of data

Question 24

Which of the following is NOT a step in problem scoping in AI?

Accepted Answer

Collecting and cleaning data

Answer

Setting boundaries and constraints

Answer

Identifying relevant stakeholders

Answer

Defining the specific problem

Question 25

What is the role of problem scoping in AI project management?

Accepted Answer

To determine the feasibility and scope of the project

Answer

To collect and label training data

Answer

To design and train the AI model

Answer

To evaluate the performance of the AI model

Question 26

True or false: AI can perfectly understand and interpret natural language.

Accepted Answer

False

Answer

Cannot be determined

Answer

Partially true

Answer

True

Question 27

True or false: AI can fully replicate human intelligence.

Accepted Answer

False

Answer

Partially true

Answer

Cannot be determined

Answer

True

Question 28

True or false: AI can perform tasks without any human input or guidance.

Accepted Answer

False

Answer

True

Answer

Cannot be determined

Answer

Partially true

Question 29

True or false: AI can fully understand and replicate human consciousness.

Accepted Answer

False

Answer

Partially true

Answer

True

Answer

Cannot be determined

Question 30

True or false: AI can predict future events with absolute certainty.

Accepted Answer

False

Answer

Partially true

Answer

Cannot be determined

Answer

True

Question 31

What is the difference between narrow AI and general AI?

Accepted Answer

Narrow AI is designed to perform specific tasks, while general AI is capable of understanding and performing any intellectual task that a human being can do.

Answer

Narrow AI is only used in the medical field, while general AI is used in various industries

Answer

Narrow AI is more advanced than general AI

Answer

Narrow AI is developed through machine learning, while general AI is developed through deep learning

Question 32

What is the role of ethics in AI development?

Accepted Answer

To ensure responsible and ethical use of AI technologies to avoid harm and bias

Answer

To maximize profits and efficiency in AI applications

Answer

To restrict the development and use of AI technologies

Answer

To prioritize technological advancements over ethical considerations

Question 33

What is the concept of machine learning in AI?

Accepted Answer

The ability of machines to learn and improve from data without being explicitly programmed

Answer

The study of human-like intelligence

Answer

The use of algorithms to automate tasks

Answer

The process of creating intelligent machines

Question 34

What is the difference between supervised learning and unsupervised learning in AI?

Accepted Answer

Supervised learning is a type of machine learning where the model learns from labeled data, while unsupervised learning is a type of machine learning where the model learns from unlabeled data

Answer

Supervised learning focuses on classification tasks, while unsupervised learning focuses on clustering and pattern recognition

Answer

Supervised learning uses deep learning algorithms, while unsupervised learning uses reinforcement learning algorithms

Answer

Supervised learning requires human intervention, while unsupervised learning is fully automated

Question 35

image is related to

Accepted Answer

<P>regression</P>

Answer

classification

Answer

<P>clustering</P>

Answer

none of these

Question 36

What is the concept of deep learning in AI?

Accepted Answer

A subset of machine learning that uses artificial neural networks to model and understand complex patterns

Answer

The study of human cognition and intelligence

Answer

The process of training AI models with large datasets

Answer

The use of algorithms to automate tasks

Related quizzes

Related quizzes