1. A _____ is a table that lists the predicted values of an AI model and the
actual/correct outcome values.
a) Classification Matrix
b) Regression Matrix
c) Confusion Matrix
d) Deep learning Matrix
2. When both predicted value of the AI model and actual value are positive,
it is called _____________
a) True Positive
b) True Negative
c) False Positive
d) False Negative
3. Statement1: The output given by the AI model is known as reality.
Statement2:The real scenario is known as Prediction.
(a)Both Statement1 and Statement2 are correct
(b)Both Statement1 and Statement2 are incorrect
(c) Statement1 is correct but Statement2 is incorrect
(d) Statement2 is correct but Statement1 is incorrect
4. Anjali has made a model which predicts the performance of students in the
various examinations in India. She collected the data of students’ performance
with respect to state, age, school and curriculum. Her model works with good
accuracy and precision value. Which of the statements given below is
incorrect?
(a) Data gathered with respect to state, age, school and curriculum is known as
Testing Data.
(b) Data given to an AI model to check accuracy and precision is Testing Data.
(c) Training data and testing data are acquired in the Data Acquisition stage.
(d) Training data is always larger as compared to testing data.
5. Amaira made a Forest Fire detector system for which she had collected the
dataset and used all the dataset to train the model. Then, she used the same
data to evaluate the model which resulted in the correct answer all the time
but was not able to perform with unknown dataset. Name the concept.
a)Best fit b) Overfitting c) underfitting d) Regression
6. Which condition of the evaluation does the following diagram indicate?
a) True Positive
b) True Negative
c) False Positive
d) False Negative
7. Which evaluation parameter takes into consideration all the correct
predictions?
a)Precision
b) Recall
c) Accuracy
d) F1 score
8. Statement 1: Overfitting is not recommended for evaluation of a model.
Statement 2: This is because the model will simply remember the whole
training set, and will therefore always predict the correct label for any point in
the training set.
(a) Both Statement 1 and Statement 2 are correct.
(b) Both Statement 1 and Statement 2 are incorrect.
(c) Statement 1 is correct but Statement 2 is incorrect.
(d) Statement 2 is correct but Statement 1 is incorrect.
9. It is one of the parameters for evaluating a model's performance which is
defined as the percentage of true positive cases versus all the cases where the
prediction is true. Which of the following evaluation parameters is this?
(a) Precision
(b)Recall
(c) F1 score
(d) accuracy
10. With respect to evaluation, for which of the following does the prediction
and reality match?
(a) True positive and False positive
(b) True positive and True negative
(c) False positive and False negative
(d) True positive and False negative
11. Which of these reflect the correct decisions by an AI model?
a) True Positive
b) True Negative
c) False Positive
d) False Negative
12. ____ is the percentage of times the predictions out of all the observations are
correct.
a) Precision Rate
b) Recall
c) Accuracy Rate
d) F1 score
13. ____ is the rate at which desirable predictions turn out to be correct.
a) Precision Rate
b) Recall
c) Accuracy Rate
d) F1 score
14. A high F1 score generally suggests:
a) A significant imbalance between precision and recall
b) A good balance between precision and recall
c) A model that only performs well on specific data points
d) The need for more training data
15. When the predicted value of the AI model is positive but actual value is
negative, it is called __________
a) True Positive
b) True Negative
c) False Positive
d) False Negative
16. The goal of evaluating an AI model is to:
a) Maximize error and minimize accuracy
b) Minimize error and maximize accuracy
c) Focus solely on the number of data points used
d) Prioritize the complexity of the model
17. In a binary classification problem, a model predicts 70 instances as positive
out of which 50 are actually positive. What is the recall of the model?
a) 50%
b) 70%
c) 80%
d) 100%
18. A teacher's marks prediction system predicts the marks of a student as 75,
but the actual mark obtained by the student is 80. What is the absolute error in
the prediction?
a) 5
b) 10
c) 15
d) 20
19. Which of the following ethical concerns is related to taking the responsibility
for the choice of evaluation metrics.
a) Bias
b) Accountability
c) Transparency
d) Translucency
20. How is the relationship between model performance and accuracy
described?
a) Inversely proportional
b) Not related
c) Directly proportional
d) Randomly fluctuating
21. What is the full form of CNN in the context of Computer Vision?
a) Convolutional Neural Network b) Computer Network Node
c) Central Neural Network d) Convoluted Network Node
2. Which of these is a key function of Computer Vision?
a) Text-to-speech conversion b) Image recognition
c) Data compression d) Speech processing
22. What is a pixel in an image?
a) Brightness tool
b) A unit of image storage
c) The smallest unit of animage
d) An image filter
23. Which model is commonly used for color representation in digital
images?
a) CMYK
b) RGB
c) HSV
d) RYB
24. What is the main use of the pooling layer in a CNN?
a) To enlarge the image
b) To reduce processing complexity
c) To convert image format
d) To sharpen image
25. Which of these is an application area of Computer Vision?
a) Facial recognition
b) Audio mixing
c) Sound detection
d) Text translation
26. What does edge detection help achieve in image processing?
a) Blur the background
b) Find object outlines
c) Add shadow effects
d)Remove image noise
27. What does resolution in an image typically indicate?
a) Number of bits
b) Number of colors
c) Width × Height in pixels
d)Color range of pixels
28. Which range represents grayscale image pixel values?
a) 1 to 128
b) 0 to 255
c) 0 to 1000
d) 1 to 256
29. What is object detection in the context of CV?
a) Changing image colors
b) Compressing images
c) Finding objects within an image
d) Saving an image
30. Which layer in a CNN is responsible for learning key features?
a) Output layer
b) Pooling layer
c) Convolutional layer
d) Input layer
31. Which of the following is not a Computer Vision task?
a) Object detection
b) Image segmentation
c) Voice modulation
d)Image classification
32. What is the core idea behind image classification?
a) Locate object boundaries
b) Assign category to image
c) Convert to grayscale
d) Measure image resolution
33. What is the role of the ReLU layer in CNN?
a) Adds shadows
b) Removes negative values
c) Enlarges feature map
d) Rotates images
34. Object detection and handwriting recognition are examples of:
a) Sound processing
b) Image compression
c) Computer Vision tasks
d) Data entry
35. What does the pixel value indicate in a grayscale image?
a) The pixel size
b) Color depth
c) Brightness level
d) Image height
36. In an RGB image, what does a pixel with all values 0 represent?
a) Maximum brightness
b) Complete darkness
c) Full saturation
d)Grayscale tone
37. Assertion: Object detection is more complex than image classification.
Reason: It involves identifying the object’s type and its position in the
image.
a) Both A and R are true; R explains A
b) Both A and R are true; R doesn't explain A
c) A is true; R is false
d) A is false; R is true
38. Assertion: Grayscale images consist of varying shades of gray and use
one byte per pixel.
Reason: Each pixel in grayscale has three intensity values ranging from 0
to 255.
a) Both A and R are true; R explains A
b) Both A and R are true; R doesn't explain A
c) A is true; R is false
d) A is false; R is true
39. What is the main goal of using CNNs in Computer Vision?
a) Text formatting
b) Image encryption
c) Pattern detection in visuals
d)File conversion
40. Which feature of NLP helps in understanding the emotions of the
people mentioned with the feedback?
(a) Virtual assistants
(b) Sentiment analysis
(c) Text classification
(d) Automatic summarization
41. Which of the following is used for finding the frequency of words in
some given text sample?
(a) Stemming
(b) Lemmatisation
(c) Bag of words
(d) None of the above
42. Machine translation feature converts _____.
(a) One language to another
(b) Human language to machine language
(c) Any human language to Programming
(d) Machine language to human language
43. Which of the following comes under NLP?
(a) Chatbots
(b) Price comparison websites
(c) Facial recognition
(d) All of the above
44. Chatbots are AI systems which
(a) Interact with humans through text or speech
(b) Are able to offer round the clock responses and handle multiple queries simultaneously
(c) Both (a) and (b)
(d) Neither (a) nor (b)
45. What do we call the process of dividing a string into component
words?
(a) Regression
(b) Word Tokenisation
(c) Classification
(d) Clustering
46. Sentence segment is the _____ step for building the NLP model.
(a) First
(b) Second
(c) Third
(d) Fourth
47. Which of these is not a stopword?
(a) This
(b) Things
(c) Is
(d) Do
48. What is the stem of the word “Making”?
(a) Mak
(b) Make
(c) Making
(d) Maker
49.What is the lemma of the word “Making”?
(a) Mak
(b) Make
(c) Making
(d) Maker
50.Which algorithms result in two things, a vocabulary of words and
frequency of the words in the corpus?
(a) Sentence segmentation
(b) Tokenisation
(c) Bag of words
(d) Text normalisation
51.Which of the following is the type of data used by NLP applications?
(a) Images
(b) Numerical data
(c) Graphical data
(d) Text and Speech
52.A corpus contains 12 documents. How many document vectors will be
there for that corpus?
(a) 12
(b) 1
(c) 24
(d)1 / 12
53.This real life application of NLP is used to provide an overview of a
news item or blog post, while avoiding redundancy from multiple
sources and maximising the diversity of content obtained. Which is this
application?
(a) Chatbot
(b) Virtual Assistant
(c) Sentiment Analysis
(d) Automatic Summarisation
54.Which of the following words represent an example of a lemma
resulting from lemmatisation for “caring” in context to Natural
Language Processing (NLP)?
(a) Care (b) Cared
(c) Cares (d) Car
55. Bag of Words is a ______ model which helps in extracting features
out of the text which can be helpful in machine learning algorithms.
(a) Data Science (DS)
(b) Virtual Reality (VR)
(c) Natural Language Processing (NLP)
(d) Computer Vision (CV)
56. Select the correct features of Smart Bot
(a) Smart-bots are flexible and powerful
(b) Coding is required to take this up on board
(c) Smart bots work on bigger databases and other resources directly
(d) All of the above
57. For _____ the whole corpus is divided into sentences. Each sentence is
taken as a different data so now the whole corpus gets reduced to
sentences.
(a) Text Regulation
(b) Sentence Segmentation
(c) Tokenization
(d) Stemming
58. Assertion (A): Stemming is a technique used to reduce an inflected
word down to its word stem.
Reason (R): For example, the words “programming,” “programmer,”
and “programs” can all be reduced down to the common word stem
“program”.
(a) Both A and R are correct and R is the correct explanation of A
(b) Both A and R are correct but R is not the correct explanation of A
(c) A is correct but R is not correct
(d) A is not correct but R is correct
59. Assertion (A): TF-IDF is a natural language processing (NLP)
technique that’s used to evaluate the importance of different words in a
sentence.
Reason (R): It’s useful in text classification and for helping a machine
learning model read words.
(a) Both A and R are correct and R is the correct explanation of A
(b) Both A and R are correct but R is not the correct explanation of A
(c) A is correct but R is not correct
(d) A is not correct but R is correct
60.
