AI PROJECT CYCLE
1. The Al project cycle provides the right framework
to guide us to our goals.
2. The Al project cycle basically consists of five
phases: problem scoping, data acquisition, data exploration, modelling, and
evaluation
3. In problem scoping, we specify the problem we want
to solve. Set a goal for it.
4. Problem scoping takes into account the various
parameters that affect the problem.
5. The underlying data for the project helps to
understand the parameters associated with the problem definition
6. Data is collected from a variety of trusted and
genuine sources. Then data is arranged using different types of visual
representations, such as graphs, databases, flowcharts, and maps. This makes it
easy to interpret the any pattern in the data. This is only the initial
analysis and is done manually.
7. After examining the pattern, the type of model to
be created is determined. To do this, research online can be used.
8. The most efficient model is the basis of your Al
project, on which you can build algorithms.
9. After the modelling is complete, you need to test
your model with the newly acquired data. The results will help you evaluate and
improve your model.
10. Finally, after the evaluation, the project cycle
is completed and you can deploy the Al model at the client site.
11. The 4Ws Problem Canvas helps identify key factors
related to the problem. The
12. "Who" block helps analyze people who are
directly or indirectly affected.
13. Under the "What block, you need to see what
resources you have.
14. The "where" block helps you find out
where the problem occurs.
15. Through the "Why" block consider what
benefits the solution will bring to stakeholders and how it will
benefit them and society.
16. The Problem Statement template helps you combine
all the important points into one template.
17. In Al modelling, data is divided into two
parts-training and testing data.
18. Training data must be authentic and is used to
teach the machine.
19. After the modelling is complete, testing data is
used to validate the Al model.
20. Data features refer to the type of data collected.
21. Data can be collected through government websites,
surveys, sensors, APIs, web scraping, and cameras
22. Al models can be classified as Learning based and
rule based.
23. In rule-based approach, the data along with the
rules are fed to the machine for training purposes.
24. One disadvantage of rule-based approach is that
the machine's learning is static. Once the training is over, the machine will
not take into account any changes made to the original training dataset.
25. In learning-based approach, the machine learns by
itself by adjusting to the new data.
26. Learning based approach is of three types
-Supervised learning, Unsupervised learning and reinforcement learning
27. In supervised learning, we feed labelled data to
the Al model and the machine learns from it.
28. In Unsupervised learning, the machine is given
unlabelled data. The Al model infers patterns from this
random data.
29. In reinforcement learning, the machine learns on its
own using reward and punishment method. blocks called nodes.
30. A neural network is divided into many layers and
each layer is then divided into many 31. The first layer of the neural network
is called the input layer. The job of the input layer is to acquire data and
transfer it to the neural network.
32. The next layer(s) are hidden layers where all the
processing happens. The final processed answer is then passed to the output
layer.
Objective Questions-
Multiple Choice Questions (MCQs)
1. …………………………………is an example of learning-based Al
a. Neural Network b.
data mining
c. data warehousing d.
None of these
2. Which of these is NOT a stage of project life
cycle?
a. Problem Scoping b.
Data Exploration
c. Data Mining d.
Evaluation/Deployment
3. K Nearest Neighbour is a………………………learning algorithm
a. Unsupervised
c. Reinforcement
4. Data Exploration means
a. Deployment of model b.
Testing of model
c. Brainstorming d.
Cleaning and normalisation of data.
5. This type of Machine Learning involves rewards and
punishment strategy
a. Supervised Learning b.
Unsupervised Learning
c. Reinforcement Learning d. None of the
Above
6. One of the most common causes of failure or delay
of Al projects is
a. Wrong Goal Setting b.
Late Testing
c. Erroneous Problem Scoping d. Wrong Prototype
7. In a Neural Network, which layer handles
user-interface
a. Input Layer b.
Hidden Layer
c. Node d.
Output Layer
8. A Supervised Learning model which takes in
continuous values of data over a period of time
a. Clustering b.
Classification
c. Regression d.
Decision Trees
9. One of the biggest sources of data nowadays for
many big companies is
a. Cortana b.
Smartphones
c. Smartwatches d.
Computer Vision
10. ……………………is a step in the Al Project Cycle where we
train and test the machine learning models to select the best one for the given problem.
a. Problem Scoping b.
Modelling
c. Data Acquisition d.
Evaluation
11. In a neural network, the layers are made up of………………………….
a. Perceptron b.
Neurons
c. Both a. and b. d.
Nodes
12. Amazon's recommender systems are an example of…………………………………………………………
a. Supervised Learning b.
Unsupervised Learning
c. Regression d.
Reinforcement Learning
13. Processing occurs in the……………..layer of a neural
network.
a. Input Layer b.
Hidden layer
c. Output Layer d.
None of these
14. The 4W Problem Canvas has the following blocks:
a. Who, How, When, Where b. Who, What,
Where, When
c. Who, What, Where, Why d. None of these
15. Consider a two-year-old child in a family. She
plays with her cat every day and recognises it. A few weeks later a family friend brings along a cat who tries
to play with the child. The child has not seen this cat earlier, but she
recognizes that many features (2 ears, eyes, walking on 4 legs) are like her
pet cat. She identifies the new animal as a cat. What kind of learning is this?
a. Supervised Learning b. Deep
Learning
c. Reinforcement Learning d. Unsupervised
Learning
16. Which one of the following is NOT a function of
the hidden layer in a neural network?
a. Receive data from the Input layer b. Pass
data to the Output layer after processing
c. Display the final answer/prediction d. Process
the data received
17. Which of the following machine learning algorithm
allows machines to automatically determine the ideal behaviour within a specific context and maximise
its performance?
a. Supervised Learning b.
Unsupervised Learning
c. Machine Learning d.
Reinforcement Learning
18. ………………………is used only to assess the performance of
a model
a. Training Data b.
Test Data
c. Bad Data d.
None of these
19. The block in 4Ws Problem Canvas identifies the
stakeholders of the project.
a. Who b.
What
c. Where d.
Why
20. Which supervised learning algorithm works on
continuous data?
a. Classification b.
Clustering
c. Deep Learning d.
Regression
21. Which of the following is correct about the rule
based approach? [CBSE
Sample Paper, 2022]
a. We cannot provide enough rules to the machine.
b. A drawback/feature for this approach is that the
learning is static.
c. Once the rules are fed into the system, it takes
into consideration any changes made in the dataset.
d. It can improve itself based on the feedbacks.
22. Choose the five stages of Al project cycle in
correct order [CBSE
Sample Paper, 2022]
a. Evaluation -> Problem Scoping-> Data Exploration->
Data Acquisition-> Modelling
b. Problem Scoping-> Data Exploration -> Data
Acquisition-> Evaluation-> Modelling
c. Data Acquisition-> Problem Scoping-> Data
Exploration -> Modelling->Evaluation
d. Problem Scoping-> Data Acquisition-> Data
Exploration->Modelling-> Evaluation
23. A business problem wherein we categorize whether
an observation is "Safe," "AtRisk”, or "Unsafe" is an example (CBSE
Sample Paper, 2022)
a. Classification b.
Clustering
c. Regression d.
Dimensionality Reduction
24. ………………………..helps us to summarise all the key
points into one single outline so that in future, whenever there is need to look back at the basis
of the problem, we can take a look at it and understand the key elements
of it. [CBSE
Sample Paper, 2022]
a. 4W Problem canvas b.
Problem Statement Template
c. Data Acquisition d.
Algorithm
25. Which of the following is incorrect? [CBSE
Sample Paper, 2022]
i. Testing data is the one on which we train and fit
our model basically to fit the parameters
ii. Training data is used only to assess performance
of model
iii. Testing data is the unseen data for which
predictions have to be made
a. i) and iii) only b. i) and ii) only
c. ii) and iii) only d.
i), ii) and iii)
26. Unscramble the letters and find the parameter that
is NOT used in evaluation stage
a. CMVETEEHAIN b.
ONSIPRICE
c. CRYAUACCC d.
ECLARL
27. Assertion (A): We can use histograms when
data is in categories (such as "Pop", "Rock",
"Jazz","Hip-Hop" etc)
Reason (R):
We use bar charts when we have continuous data (such as a person's height or
weight)
a. (A) is false but (R) is true b.
(A) is true but (R) is false
c. Both (A) and (R) are true d. Both (A) and
(R) are false
28. Assertion (A): The training data should be
authentic and relevant to the problem statement scoped. [CBSE, 2022]
Reason (R): It
increases the Al Project efficiency.
a. Both Assertion (A) and Reason (R) are correct and
Reason (R) is the correct explanation of Assertion (A).
b. Both Assertion (A) and Reason (R) are correct, but
Reason (R) is not the correct explanation of Assertion (A).
c. Assertion (A) is correct, but Reason (R) is not
correct.
d. Assertion (A) is not correct, but Reason (R) is
correct.
29. ……………………..involves collecting data from various
authentic source such as reliable websites, observations, surveys. (CBSE,
2022)
a. Data Acquisition b.
Data Evaluation
c. Data Testing d.
Data Modelling
30. Accuracy, Recall, Precision, F1 Score are the
parameters to calculate the efficiency under ………[CBSE, 2022]
a. Data Acquisition b.
Data Evaluation
c. Data Testing d.
Data Modelling
31. Drawback/s of Rule-based approach is/are:
i. The learning is static.
ii. Any changes made to the original data will not be
considered.
iii. Once trained the model cannot improvise on the
basis of feedback.
a. All of the above statements are correct b. Statements I and
il are correct
c. Statements il and ill are correct d.
Statements i and ill are correct
32. In………………………... design is adaptive to change in
data, which results in dynamicity of a model. [CBSE, 2022]
a. Learning-based Approach b. Rule-based
Approach
c. Data Acquisition d.
Data Modelling
33. ……………………..are modelled on human brain, it is
essentially a machine learning algorithm, useful for solving problems when the dataset is large. [CBSE, 2022]
a. Computer Vision b.
Data Science
c. Natural Language Processing d. Neural Network
34. …………………….is not a Learning-based Approach where Al
model gets trained on the data fed to it and then is able to design a model which is adaptive to the
change in data. [CBSE, 2022]
a. Supervised Learning b.
Unsupervised Learning
c. Reinforcement Learning d. Enforcement
Learning
35. Unsupervised Learning is divided into the
following two categories. Identify the correct one. (CBSE, 2022)
a. Rule-based and Learning-based b.
Classification and Clustering
c. Clustering and Dimensionality Reduction d. Classification and
Regression
36. The 4 Ws Problem canvas helps in identifying the
key elements related to the problem. 4 Ws Problem canvas is a part of: (CBSE,
2022)
a. Problem Scoping b. Data Acquisition
c. Modelling d.
Evaluation
37. The ……………………. block of 4 Ws Problem canvas helps
in analysing the people getting affected directly or
indirectly due to it. (CBSE,
2022)
a. Who b.
What
c. Where d.
Why
38. During Data Acquisition feeding previous data into
the machine is called:
a. Training Data d.
Evaluating Data
c. Testing Data b.
Predicting Data
39. ……………….is one of the types of Supervised Learning
Model, where data is classified according to labels and data need not to be continuous. [CBSE,
2022]
a. Regression b.
Classification
c. Clustering d.
Dimensionality reduction
40 Evaluation is the process of understanding the
reliability of any Al model, based on outputs by feeding test dataset into the
model and comparing with actual answers. Therefore it must be followed by: (CBSE,
2022)
a. Problem Scoping b.
Data Acquisition
c. Data Exploration d.
Modelling
41. Classification and Regression are ……………………., and
approach followed is……………………..[CBSE, 2022]
a. Supervised, Learning b. Unsupervised,
Learning
c. Reinforcement, Learning d. Supervised,
Rule-based
42. ……………………………refers to the unsupervised learning
algorithm which can cluster the unknown data according to the patterns or
trends identified out of it. [CBSE,
2022]
a. Regression b.
Classification
c. Clustering d.
Dimensionality Reduction
-Answers
|
1. a |
2. b |
3. b |
4. d |
5. c |
6.c |
7. d |
8. c |
9. b |
|
10. b |
11. d |
12. b |
13. b |
14. c |
15. d |
16. c |
17. c |
18. b |
|
19. a |
20. d |
21. b |
22. d |
23. a |
24. b |
25. b |
26. a |
27. d |
|
28. a |
29. a |
30. b |
31. a |
32. a |
33. d |
34. d |
35. c |
36. a |
|
37. a |
38. a |
39. b |
40. d |
41. a |
42. c |
|
||
Subjective Questions-
Very Short/Short Answer Questions
A. Fill in the Blanks.
1. ...............learning model works on unlabelled
dataset/random data.
2. ……………………..stage follows after Data Exploration in
Al project cycle.
3. "Once trained, the model cannot learn from its
mistakes." This statement is true for……………..types of models
4. Under. block in 4Ws Problem Canvas, we gather
evidence to prove that the problem selected actually exists.
5. Data Acquisition means collecting data from……………….and
……………………sources.
6. A machine learns by finding ……………….. in data.
7. Each node in the hidden layer of a neural network
has its own……………………………….
8. In a grouping model, people are grouped on the
basis of their shopping patterns with respect to their purchases on an
ecommerce website. This type of learning is………………………
9. After model generation…………………..of the model takes
place
10. An Al model can predict the marks of a student if
number of hours studied is provided. The learning algorithm being used is ……………………………………..
-Answers
1. unsupervised 2.
Modelling 3. Rule-based 4. What 5. reliable and authentic
6. patterns 7.
ML Algorithm 8. Clustering 9.
testing 10. regression
B. State whether True or False.
1. Once the model is complete, the problem statement
needs to be evaluated again.
2. If we keep reducing the dimensions of an entity,
more and more information is lost.
3. Al models using supervised learning are used to
identify relationships, patterns and trends out of the data fed into them.
4. Various types of graphical representations are used
in the Data Acquisition phase.
5. In the "Why" block, we think about how
the society will benefit from the Al model.
Answers
1. False 2.
True 3. False 4. False 5. True
C. Very Short Answer Type Questions
1. List 4 methods to collect data.
Ans: a. Surveys b. open-sourced websites
hosted by the government
c.
Interviews d. Sensors
2. Explain one similarity and one point of difference between
Regression and Classification.
Ans. Similarity: Both are Supervised Learning Algorithms.
Difference:
|
Regression |
Classification |
|
This algorithm is used to predict values such as price,
salary, age, etc. |
Classification algorithms are used to group values
such as Male or Female, True or False, Spam or Not Spam into categories. |
3. Differentiate between Data Acquisition and Data
Exploration.
Ans.
|
Data Acquisition |
Data
Exploration |
|
Collecting data for the Al project. |
Cleaning and preparing the data that has been
collected. |
|
Collection methods Surveys, Observations, Interviews |
Data is studied/arranged visually to understand
patterns |
4. Differentiate between rule based and learning based
approach in Machine Learning.
Ans.
|
Rule based Approach |
Learning based
Approach |
|
The machine follows the rules or instructions mentioned
by developer and performs its task accordingly. |
The Al model gets trained on the data fed to it and
then is able to design a model which is adaptive to the change in data. |
|
Learning in this model is static. |
The learning in this model is dynamic. |
5. What is an algorithm? List one algorithm that you
have studied. What does it do?
Ans. An
algorithm is a set of rules given to an Al program to help it learn on its own.
Regression is a supervised learning algorithm and gives predictions based on
continuous data.
6. Differentiate between training data and testing
data.
Ans.
|
Training Data |
Test Data |
|
In a dataset, data is split into 2 sets. 80% is 20% is
training data. |
20% of the data is test data. |
|
This data is used to train the model. |
This data has not been seen by the model so is used
to assess the accuracy of the model. |
7. What is the purpose of Data Exploration?
Ans. Data
exploration involves analysing the large dataset in an unstructured way to understand
initial patterns and characteristics. Data visualizations tools like charts are
also used to understand the patterns.
8. Name all the stages of the Al Project Cycle in
order.
Ans. Problem
Scoping, Data Acquisition, Data Exploration, Modelling and Evaluation.
9. Differentiate between classification and
clustering.
Ans.
|
Classification Supervised
Learning Algorithm |
Clustering Unsupervised
Learning Algorithm |
|
The Input data is grouped according to their corresponding
class labels. |
The Input data is grouped on their similarity without the help of class labels. |
10. Differentiate between supervised and unsupervised
learning. Also name one algorithm used in each learning.
Ans.
|
Supervised Learning |
Unsupervised
Learning |
|
Supervised learning algorithms are trained using
labelled data. |
Unsupervised Learning Algorithms are trained using
unlabelled data. |
|
Input data is provided to the model along with the
output. |
Only input data is provided to the model. |
|
Algorithm-Regression |
Algorithm-Clustering |
11. Explain the term 'Problem Scoping'.
Ans. Problem
scoping is the first stage of an Al Project cycle. During this stage, we set
the goal of our Al project by stating the problem it must solve. This can be
done by using the 4Ws Problem Canvas.
12. List any four features of a neural network.
Ans. a.
Neural networks are modelled on the human brain.
b. They
are able to automatically extract features without input from programmer.
c. Every
neural network node is essentially a machine learning algorithm.
d. It is
useful for solving problems for which the dataset is very large.
13. Name the machine learning algorithms.
a.
Handles continuous data
b. Is used to find trends or patterns from
the data
c.
Works on discrete data set
d.
This algorithm can be used to solve both classification and regression
problems.
Ans. a.
Regression b.
Clustering c.
Classification d.
KNN
14. During which stage of the Al project cycle should
we take care of Al Ethics? What are the challenges to this process?
Ans. During the development of the Al model (modelling
phase), care should be taken that the programmer is Including data,
instructions, etc. which provides protection of all liberties for all citizens
as per the fundamental rights of the country. There are two main challenges -
One is getting access to high quality and standardized datasets and second is
being able to find skilled programmers who can develop reliable and
high-quality machines.
15. Name 4 tools used for visual representation.
Ans. Graphs:
Line, Bar, Histograms
Pictograms,
Infographics and Maps
Long
Answer Questions
1. Briefly explain the working of a neural network
with the help of a diagram.
Ans. A
Neural Network is divided into multiple layers and each layer is further
divided into several blocks called nodes. Each node has its own task to
accomplish which is then passed to the next layer.
a. The first layer of a Neural Network is known as the
input layer. The job of an input layer is to acquire data and feed it to the
Neural Network. No processing occurs at the input layer.
b. Next to it, are the hidden layers. Hidden layers
are the layers in which the whole processing occurs.
c. The last hidden layer passes the final processed
data to the output layer which then gives it to the user as the final output.
2. What is dimensionality reduction? Give one advantage
and one disadvantage of dimensionality reduction.
Ans.
Dimensionality reduction is the transformation of data from high-dimension to
low-dimension. The low-
dimensional representation retains some meaningful
properties of the original data.
Advantages: It
helps in data compression and takes up less space.
Disadvantage:
Some amount of data loss is unavoidable.
3. Explain Supervised Learning. Give two applications
also.
Ans.
Supervised learning is a type of machine learning in which a machine is trained
with "labelled" data and the machine uses that data to predict the
output. "Labelled" data means that the input data is already marked
with the correct output. This is similar to students learning under the
supervision of a teacher.
The goal of the learning algorithm is to find a
mapping function that maps the input variable (x) to the output
variable (y). Applications of supervised learning
include image classification, and spam filtering.
4. Give one point of similarity and 3 differences
between Artificial Neural Networks and the human brain.
Ans.
Similarity-Both can learn.
Difference
|
Artificial Neural Network (ANN) |
Human Brain |
|
1. Once an ANN is fully trained, information is not
forgotten. |
1 Humans can forget the information |
|
2. Results/Calculations are accurate. |
2. Results may not be accurate. |
|
3. Uses high speed for processing |
3. Gets tired after several attempts |
5. Explain Reinforcement Learning with an example.
Ans. Reinforcement learning is a machine learning algorithm that interacts with its environment and produces actions. For each good action, the machine gets positive feedback, and for each bad action, the machine gets negative feedback or penalty. The machine learns from experience as there is no labelled data. Example: Deep Blue Machine was trained to play chess; IBM Watson was trained to play the game Jeopardy.
Competency Based
Questions
1. Assume that you are working at MyFlight which is a major airlines company and that you have noticed thatthe way passengers board your planes is an inefficient use of time and resources. On an average, the current boarding system wastes about four minutes per boarding. This wastes about 35000 rupees per day across all flights. The boarding protocols make the company less competitive and thus create an unfavourable brand image. Using a modified boarding, passengers can board the plane from the sides rather than from the back to the front. This will eliminate four minutes of waste. Taking this as the problem, choose which of the following would be the ideal problem statement template.
a. Our
passengers have a problem that it takes more time when one has to board the
plane. An ideal solution would be to use different airlines.
b. Our
passengers have a problem that the current boarding system wastes time while
waiting in the airport. An ideal solution would be to board the plane before
the airline crew gets into the plane.
c. Our
airlines have a problem that the current boarding system wastes four minutes of
time when passengers aboard the plane. An ideal solution would be to board the
plane from the sides rather than from the back to the front.
d. Our
airlines have a problem that it takes more time when passengers have to board
the plane. An ideal solution would be to sell the airlines
2. i.
Understand and inspect the web page to find the HTML markers associated with
the information we want.
ii.
Use Python libraries to pull out data from the HTML page.
iii.
Manipulate the collected data to get it in the form we need.
The above
given steps are for collecting data from which of the following data sources?
a.
Cameras b. Sensors c. Surveys d. Web scraping
3. Data
about the houses such as square footage, number of rooms, features, whether a
house has a garden or not, and the prices of these houses, i.e., the
corresponding labels are fed into an Al machine. By leveraging data coming from
thousands of houses, their features and prices, we can now train the model to
predict a new house's price. This is an example of
a.
Reinforcement learning b.
Supervised learning
c. Unsupervised learning d. None of the
above
4. A scenario is given to you below. Read it and
answer the questions that follow:
Late one night, a car ran over a pedestrian in a
narrow by street and drove away without stopping. A policeman who saw the
vehicle leave the scene of the accident reported it moving at very high speed.
The accident itself was witnessed by six bystanders. They provided the
following conflicting accounts of what had happened:
·
It was a Toyota
and its headlights were turned off;
·
It was a grey
Audi. It was a red car driven by a woman;
·
The car was
moving at high speed and its headlights were turned off;
·
The car did have
license plates; it wasn't going very fast;
·
The car didn't
have license plates; the driver was a man;
When the
car and its driver were finally apprehended, it turned out that only one of the
six eyewitnesses gave a fully correct description. Each of the other five
provided one true and one false piece of Information. Keeping that in mind, can
you determine the following:
i. What was the car's brand? ii. What
was the colour of the car?
iii. Was the car going fast or slow? iv.
Did it have license plates?
v. Were its headlights turned on? vi.
Was the driver a man or a woman?
a. i)-> TOYOTA; ii)-> GREY: H)-> FAST:
iv)-> NO; v)-> NO; vi)-> WOMAN
b. i)-> AUDI; ii)-> RED; ii)->SLOW; iv)->
NO; v) YES; vi)-> WOMAN
c. i)-> AUDI: i)-> RED; iii)-> FAST; iv)->
YES; v)-> NO; vi)-> MAN
d. i)-> TOYOTA; ii)-> RED; iii)->SLOW:
iv)-> NO; v)-> NO: vi)-> MAN
5. The world is competitive nowadays. People face
competition in even the tiniest tasks and are expected to give their best at
every point in time. When people are unable to meet these expectations, they
get stressed and could even go into depression. We get to hear a lot of cases
where people are depressed due to reasons like peer pressure, studies, family
issues, relationships, etc. and they eventually get into something that is bad
for them as well as for others. So, to overcome this, Cognitive Behavioural
Therapy (CBT) is considered to be one of the best methods to address stress as
it is easy to implement on people and also gives good results. This therapy
includes understanding the behaviour and mindset of a person in their normal
life. With the help of CBT, therapists help people overcome their stress and
live a happy life.
For the situation given above,
i. Write the problem statement template
ii.List any two sources from which data can be
collected
iii. How do we explore the data? [CBSE
Sample Paper, 2020]
Ans. i. The
problem statement template for the given scenario would be
|
Our |
people undergoing stress |
Who? |
|
|
have a problem that they are not being able to share
their feelings |
What? |
|
While |
they need help to vent out their emotions |
Where? |
|
An ideal solution would |
To provide a platform to share their thoughts
anonymously and suggest help whenever required. |
Why? |
ii. Data can be collected from one of the following
sources:
a. surveys b.
observing therapist's sessions
c. databases available on the internet d. interviews
iii. Once
the textual data has been collected, it needs to be processed and cleaned so
that an easier version can be sent to the machine. Thus, the text is normalized through various steps and is lowered to minimum vocabulary since the machine
does not require grammatically correct statements but the essence of it.
6. Choose the correct option describing the features
of Neural Network. These are systems modelled on Human Brain and Nervous
System: (CBSE,
2022)
i. It is essentially a machine learning algorithm.
ii. It is useful when solving the problems for which
the data set is very large.
iii. They are able to extract feature without input
from the programmer.
a. Statements i and ii are correct b.
Statements i and iii are correct
c. Statements ii and iii are correct d. All of
the above statements are correct
7. Vijender has to maintain inventory on a daily
basis, it usually takes 3-4 hours for a helper to take out raw material, which
in turns delays the production cycle and also delays the updating of stock. His
manager has identified the problem and looking for a possible solution. This is
the task under:
a. Problem Scoping b.
Data Exploration
c. Data Evaluation d.
Neural Network (CBSE,
2022)
8. The following are the key elements of………… [CBSE,
2022]
i. Who are the stakeholders?
ii. What is the problem?
iii. Where does the problem arise?
iv. Why do you believe that the problem is worth
solving?
a. 4 Ws Problem Canvas b.
Accuracy
c. Precision d.
Recall
-Answers 2.
d 3. b 4. c 6. d 7.
a 8. a
A. Tick the correct option.
1. How many stages are there in the Al Project Cycle?
a. 4 b.
5 c. 6 d. 7
2. Which of the following takes into account the
various parameters that affect the problem?
a. Evaluation b.
Problem Scoping c.
Modelling d. None of these
3. Data Exploration means
a. Deployment of model b. Testing
of model
c. Brainstorming d.
Cleaning and normalization of data
4. In a Neural Network, the result is shown on which
layer?
a. Input
layer b.
Hidden layer
c. Output
layer d.
None of these
5. Which supervised learning algorithm works on
continuous data?
a. Classification b.
Clustering c. Deep Learning d. Regression
B. Short answer type questions.
1. Name all the stages of the Al Project Cycle.
2. What are various methods to collect data? 3. What
is the basis of an efficient model?
C. Long answer type questions.
1. What is the difference between learning and
rule-based models?
2. What is Problem Statement template? Explain in
detail.
3. What is the difference between supervised and
unsupervised learning?
Answers 1. B 2. b 3. d 4. c 5. d
Unit Test-5
A. Tick the correct option.
1. How many stages are there in the Al Project Cycle?
a. 4 b.
5 c. 6 d. 7
2. Which of the following takes into account the
various parameters that affect the problem?
a. Evaluation b.
Problem Scoping c.
Modelling d. None of these
3. Data Exploration means
a. Deployment of model b. Testing
of model
c. Brainstorming d.
Cleaning and normalisation of data
4. In a Neural Network, the result is shown on which
layer?
a. Input
layer b.
Hidden layer
c. Output
layer d.
None of these
5. Which supervised learning algorithm works on
continuous data?
a. Classification b.
Clustering c. Deep Learning d. Regression
B. Short answer type questions.
1. Name all the stages of the Al Project Cycle.
2. What are various methods to collect data? 3. What
is the basis of an efficient model?
C. Long answer type questions.
1. What is the difference between learning and
rule-based models?
2. What is Problem Statement template? Explain in
detail.
3. What is the difference between supervised and
unsupervised learning?
Answers 1. B 2. b 3. d 4. c 5. d
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