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Machine Learning and Deep Learning MCQs

Machine Learning and Deep Learning MCQs: Anyone can sharpen their knowledge of machine learning and deep learning with these multiple choice questions and answers.

Multiple choice questions and answers:

MCQs

Question 1:

Which of the factors affect the performance of learner 
system does not include?

 A. Representation scheme used
 B. Training scenario
 C. Type of feedback
 D. Good data structures

Answer: D

Question 2:

What is Machine learning?

A. The autonomous acquisition of knowledge through the 
   use of computer programs
B. The autonomous acquisition of knowledge through the 
   use of manual programs
C. The selective acquisition of knowledge through the  
   use of computer programs
D. The selective acquisition of knowledge through the 
   use of manual programs

Answer: A

Question 3:

In language understanding, the levels of knowledge that 
does not include?

A. Phonological
B. Syntactic
C. Empirical
D. Logical

Answer: C

Question 4:

 Different learning methods do not include?

A. Memorization
B. Analogy
C. Deduction
D. Introduction

Answer: D

Question 5:

A model of language consists of the categories which 
does not include?

A. Language units
B. Role structure of units
C. System constraints
D. Structural units

Answer: D

Question 6:

In language understanding, the levels of knowledge that 
does not include?

A. Syntactic
B. Phonological
C. Logical
D. Empirical

Answer: D

Question 7:

Among the following which is not a horn clause?

A. p
B. Øp V q
C. p → q
D. p → Øq

Answer: D

Question 8:

What is a top-down parser?

A. Begins by hypothesizing a sentence (the symbol S) and 
   successively predicting lower level constituents  
   until individual preterminal symbols are written
B. Begins by hypothesizing a sentence (the symbol S) and 
   successively predicting upper level constituents 
   until individual preterminal symbols are written
C. Begins by hypothesizing lower level constituents and 
   successively predicting a sentence (the symbol S)
D. Begins by hypothesizing upper level constituents and 
   successively predicting a sentence (the symbol S)

Answer: A

Question 9:

The action ‘STACK(A, B)’ of a robot arm specify to
 
A. Place block B on Block A
B. Place blocks A, B on the table in that order
C. Place blocks B, A on the table in that order
D. Place block A on block B

Answer: D

Question 10:

Which of the following are ML methods?

A. based on human supervision
B. supervised Learning
C. semi-reinforcement Learning
D. All of the above

Answer: D

Question 11:

In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?

A. mini-batches
B. optimizedparameters
C. hyperparameters
D. superparameters

Answer: C

Question 12:

The model will be trained with data in one single batch is known as ?

A. Batch learning
B. Offline learning
C. Both A and B
D. None of the above

Answer: C

Question 13:

Different learning methods does not include?

A. Introduction
B. Analogy
C. Deduction
D. Memorization

Answer: A

Question 14:

A model of language consists of the categories which does not include ________.

A. System Unit
B. structural units.
C. data units
D. empirical units

Answer: B

Question 15:

The action _______ of a robot arm specify to Place block A on block B.

A. STACK(A,B)
B. LIST(A,B)
C. QUEUE(A,B)
D. ARRAY(A,B)

Answer: A

Question 16:

p → 0q is not a?

A. hack clause
B. horn clause
C. structural clause
D. system clause

Answer: B

Interview type MCQs

1. What is representation in deep learning?

A. It is a way to look at data to represent or encode
B. It gets closer to the expected output
C. RGB and HSV are two different examples of representations
D. All of the above

Answer: D

2. What is learning in deep learning?

A. Learning, in the context of machine learning, describes an automatic search
 process for better representations
B. A process that is “learned” from exposure to known examples of inputs and
 outputs
C. Learning in school attending 5th grade
D. Answers A & B
E. Answers B & C

Answer: D

3. What is hypothesis space in deep learning?

A. ML/DL algorithms merely searching through a predefined set of
 operations, called a hypothesis space
B. Searching for useful representations of some input data, within a predefined space of possibilities, using guidance from a feedback signal
C. Answers A & B
D. None of the above

Answer: C

4. What is deep in deep learning?

A. The deep in deep learning is a reference to any kind of deeper understanding achieved by the approach
B. It stands for the idea of successive layers of representations in deep learning
C. Answers A & B
D. None of the above

Answer: B

5. What is depth in deep learning?

A. How many layers contribute to a model of the data is called the depth of the model
B. No. of successive layers of representations
C. Answers A & B
D. None of the above

Answer: C

6. What is shallow learning in deep learning?

A. Machine learning tend to focus on learning only one or two layers of representations of the data
B. Machine learning tend to focus on learning 10 layers of representations of the data
C. Machine learning tend to focus on learning 512 layers of representations of the data
D. Machine learning tend to focus on learning 64 layers of representations of the data

Answer: A

7. What are deep neural networks in deep learning?

A. In deep learning, layered representations are (almost always) learned via models
   called deep neural networks, structured in literal layers stacked on top of each
   other
B. These networks are the brain neurons studying in neurobiology
C. These are models of human brain
D. Neural network is a cell in brain

Answer: A

8. Is the statement below true?

The term neural network is a reference to neurobiology, but although some of the central concepts in deep learning were developed in part by drawing inspiration from our understanding of the brain, deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models

Answer: Yes 
Answer: No

Answer: Yes

9. Which statement is true?

A. Deep learning is a mathematical framework for learning representations from data
B. Deep learning is a biological framework for learning representations from brain
   data
C. Deep learning is an analogue framework for learning representations from data
D. Deep learning is a digital framework for learning representations from data

Answer: A

10. What are layers in deep learning?

A. Deep neural networks do this input-to-target mapping via a deep sequence of
   simple data transformations called layers and that these data transformations
   are learned by exposure to examples
B. It is a sort of membrane in brain
C. None of the above
D. All of the above

Answer: A

11. Find true or false?

The specification of what a layer does to its input data is stored in the layer’s
weights, which in essence are a bunch of numbers. In technical terms, we’d say that the transformation implemented by a layer is parameterized by its weights. Weights are also sometimes called the parameters of a layer.

A. True 
B. False

Answer: A

12. Find true or false?

Learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets

A. True 
B. False

Answer: A

13. Is loss function same as objective function?

A. True
B. False

Answer: A

14. What is loss function in deep learning?

A. To control the output of a neural network, you need to be able to measure how far this output is from what you expected
B. To calculate loss in banks
C. These are true targets of data
D. These are the predicted values only

Answer: A

15. Answer true or false as per statement below:

The loss function takes the predictions of the network and the true target (what you wanted the network to output) and computes a distance score, capturing how well the network has done
A. True 
B. False

Answer: A

16. Which statement is true?

A. The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score
B. The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will higher the loss score
C. Both of the above
D. None of the above

Answer: A

17. What is training loop in deep learning?

A. With every step in the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases
B. It is repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function
C. All of the above are true
D. None of the above are true

Answer: C

18. What is decision boundary in classification problems?

A. A decision boundary can be thought of as a line or surface separating your 
   training data into two spaces corresponding to two categories.
B. The data is mapped to a new high-dimensional representation where the decision
   boundary can be expressed as a hyperplane (if the data was two-dimensional)
C. Both of the above are true
D. None of the above are true

Answer: C

19. What is kernel function?

A. A kernel function is a computationally tractable operation that maps any two points in your initial space to the distance between these points in your target representation space, completely bypassing the explicit computation of the new representation
B. It belongs to classification problems
C. All of the above
D. None of the above

Answer: C

20. SVM stands for ?

A. Support Vector Machine
B. Support Vector Machanism
C. Super Visual Machine
D. Support Vector Model

Answer: A

21. What are Decision Trees?

A. Decision trees are flowchart-like structures that let you classify input data points or predict output values on given inputs
B. These are decision makers for land owners
C. All of the above
D. None of the above

Answer: A

22. What is Random Forest?

A. Random Forest algorithm introduced a robust, practical take on decision-tree learning that involves building a large number of specialized decision trees and then ensembling their outputs. Random forests are applicable to a wide range of problems
B. It is the second-best algorithm for any shallow machine-learning task
C. All of the above
D. None of the above

Answer: C

23. What are Gradient Boosting Machines?

A. A gradient boosting machine, much like a random forest, is a machine-learning
 technique based on ensembling weak prediction models, generally decision trees
B. It uses gradient boosting, a way to improve any machine-learning model by iteratively training new models that specialize in addressing the weak points of the previous models
C. Applied to decision trees, the use of the gradient boosting technique results in models that strictly outperform random forests most of the time, while having similar properties
D. All of the above 
E. None of the above

Answer: D

24. How deep learning learns from data?

A. The incremental, layer-by-layer way in which increasingly complex representations are developed, and the fact that these intermediate incremental representations are learned jointly i.e each layer being updated to follow both the representational needs of the layer above and the needs of the layer below
B. Deep learning learns from input data only
C. All of the above
D. None of the above

Answer: A

25. The two key ideas of deep learning for computer vision:

A. Convolutional neural networks and backpropagation
B. Deep neural networks and kernel functions
C. Support Vector Machines and loss functions
D. None of the above

Answer: A

26. Three technical forces are driving advances in machine learning:

A. Hardware, Datasets & benchmarks, and Algorithmic advances
B. Super computers only
C. Pen and a piece of paper
D. None of the above
E. All of the above

Answer: A

27. Overfit model?

A. A model that learns the training dataset too well, performing well on the training dataset but does not perform well on a testing dataset
B. A model that fails to sufficiently learn the problem and performs poorly on a training dataset and does not perform well on a testing dataset
C. All of above
D. None of above

Answer: A

28. Example of reinforcement learning in machine learning?

A. Parking can be achieved by learning automatic parking policies
B. Enjoying ice cream sitting in automatic car
C. Reinforced learning is the recommendation on Youtube
D. Machines are super machines, one can do every task

Answer: A & C

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