Table of contents |
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Multiple Choice Questions (MCQs) |
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Fill in the Blanks |
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True or False |
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Short Ans Questions |
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Long Ans Questions |
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Q1: How many stages are there in the AI Project Cycle?
a) Three
b) Four
c) Five
d) Six
Ans: c) Five
The AI Project Cycle consists of five distinct stages, which help in systematically approaching AI projects.
Q2: What is the first stage of the AI Project Cycle?
a) Data Acquisition
b) Problem Scoping
c) Modeling
d) Evaluation
Ans: b) Problem Scoping
The first stage, Problem Scoping, is crucial as it defines the problem to be addressed by the AI project.
Q3: Which block of the 4Ws Problem Canvas identifies the people affected by the problem?
a) What
b) Where
c) Why
d) Who
Ans: d) Who
The 'Who' block helps analyze the individuals and groups impacted by the problem, aiding in stakeholder identification.
Q4: What is the purpose of the 'What' block in the 4Ws Problem Canvas?
a) To determine the context of the problem
b) To identify stakeholders
c) To determine the nature of the problem and gather evidence
d) To list benefits of the solution
Ans: c) To determine the nature of the problem and gather evidence
The 'What' block focuses on defining the problem's nature and collecting evidence to substantiate its existence.
Q5: Which modeling approach involves feeding the machine data along with predefined rules?
a) Learning-Based Approach
b) Rule-Based Approach
c) Supervised Learning
d) Unsupervised Learning
Ans: b) Rule-Based Approach
The Rule-Based Approach relies on providing data and explicit rules for the machine to make predictions.
Q6: What is a key characteristic of neural networks?
a) They require manual feature extraction
b) They automatically extract data features
c) They are limited to small datasets
d) They do not involve hidden layers
Ans: b) They automatically extract data features
Neural networks are designed to automatically extract relevant features from data, eliminating the need for manual extraction.
Q7: Which Python tool is recommended for interactively developing AI-related projects?
a) Anaconda Prompt
b) Jupyter Notebook
c) Virtual Environment
d) Python Shell
Ans: b) Jupyter Notebook
Jupyter Notebook is widely used for interactive development, providing an intuitive interface for coding and visualizations.
Q8: What is the purpose of a virtual environment in Python?
a) To execute Python code directly
b) To isolate project dependencies
c) To install Jupyter Notebook
d) To compile Python code
Ans: b) To isolate project dependencies
Virtual environments are essential for isolating different project's dependencies, preventing conflicts.
Q9: Which Python operator is used to calculate the remainder of a division?
a) /
b) //
c) %
d) **
Ans: c) %
The '%' operator is specifically used in Python to find the remainder after division.
Q10: What does the 'Why' block of the 4Ws Problem Canvas focus on?
a) Identifying stakeholders
b) Determining the context of the problem
c) Listing benefits of the solution for stakeholders
d) Gathering evidence for the problem
Ans: c) Listing benefits of the solution for stakeholders
The 'Why' block assesses the advantages that stakeholders will gain from the proposed solution.
Ans: goal
The goal of an AI project defines its primary objectives and desired outcomes, guiding the development process.
Q2: The __________ Canvas helps summarize the key elements of the problem into a single template.
Ans: Problem Statement
The Problem Statement Template condenses key aspects of the 4Ws Problem Canvas into a comprehensive format for clarity.
Q3: In the __________ stage, data is collected to form the base of the AI project.
Ans: Data Acquisition
The Data Acquisition stage is crucial as it involves gathering the necessary data that underpins the entire AI project.
Q4: __________ Learning involves training a model with labeled data.
Ans: Supervised
Supervised Learning uses labeled datasets to train models, enabling accurate predictions based on input data.
Q5: The __________ layer of a neural network is responsible for providing the final output to the user.
Ans: output
The output layer in a neural network delivers the final results to the user, completing the processing cycle.
Ans: False
The Rule-Based Approach is considered static and does not adapt to new data after the initial training phase.
Q2: The 4Ws Problem Canvas includes Who, What, Where, and Why blocks to analyze a problem.
Ans: True
The 4Ws Problem Canvas effectively utilizes the Who, What, Where, and Why elements for comprehensive problem analysis.
Q3: Data features refer to the type of data needed to address a problem, such as salary or increment percentage.
Ans: True
Data features are indeed the specific types of data collected to solve a problem, like salary amounts or increment percentages.
Q4: Unsupervised learning models always require labeled data to identify patterns.
Ans: False
Unsupervised learning models utilize unlabeled data to discover patterns, such as through clustering techniques.
Q5: Jupyter Notebook can only be used with Python and not with other programming languages.
Ans: False
Jupyter Notebook supports multiple programming languages, including R, Julia, and others, not just Python.
Q.1: What is the purpose of Problem Scoping in the AI Project Cycle?
Ans: Problem Scoping involves setting the goal for an AI project by defining the problem to be solved. It includes analyzing parameters that affect the problem to make the picture clearer.
Q.2: Explain the role of the "Who" block in the 4Ws Problem Canvas.
Ans: The "Who" block identifies the stakeholders affected directly or indirectly by the problem. It analyzes who faces the problem and what is known about them to understand who will benefit from the solution.
Q.3: What is the difference between a Rule-Based Approach and a Learning-Based Approach in AI modeling?
Ans: The Rule-Based Approach involves feeding predefined rules and data to the machine, resulting in static learning that cannot adapt to new data. The Learning-Based Approach allows the machine to learn from data, adapt to changes, and modify its model dynamically.
Q.4: Why are virtual environments useful when working with Python projects?
Ans: Virtual environments isolate project dependencies, preventing conflicts between projects with different requirements, such as different Python versions. This ensures that each project’s dependencies do not affect the base environment or other projects.
Q.5: Describe the role of the input layer in a neural network.
Ans: The input layer acquires data and feeds it into the neural network without processing it. It serves as the entry point for data to be passed to the hidden layers for processing.
Q.1: Explain the five stages of the AI Project Cycle.
Ans: The AI Project Cycle consists of five stages:
Q.2: Describe the 4Ws Problem Canvas and how each block contributes to problem scoping.
Ans: The 4Ws Problem Canvas helps identify key elements of a problem:
Q.3: Discuss the difference between supervised and unsupervised learning models with examples.
Ans: Supervised learning uses labeled data to train models, where each data point is tagged with a label. For example, a dataset with images of apples and bananas labeled as such allows the model to predict whether a new image is an apple or banana. Unsupervised learning uses unlabeled data to identify patterns, such as clustering or dimensionality reduction. For instance, clustering can group unknown data based on patterns, while dimensionality reduction simplifies high-dimensional data like words in NLP. Supervised learning relies on predefined labels, while unsupervised learning discovers patterns independently.
Q.4: Explain how neural networks work, including the roles of the input, hidden, and output layers.
Ans: Neural networks mimic human brain neurons to process data and solve tasks, especially with large datasets like images. They consist of:
Q.5: Evaluate the importance of creating a virtual environment for Python projects and how it is done using Anaconda.
Ans: Virtual environments are critical for Python projects because they isolate project dependencies, preventing conflicts between projects with different requirements, such as different Python versions. This ensures that dependencies do not affect the base environment or other projects, maintaining stability and compatibility. Using Anaconda, a virtual environment is created as follows:
31 videos|79 docs|8 tests
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1. What is the AI project cycle, and what are its main phases? | ![]() |
2. How does data preparation impact the success of an AI project? | ![]() |
3. What are common challenges faced during the AI project cycle? | ![]() |
4. Why is model evaluation important in the AI project cycle? | ![]() |
5. What role does deployment play in the AI project cycle? | ![]() |