# Sequence Models Interview Question-Answer Part – 1

Sequence Models Interview Question- – 2

Q.1 The main drawback of basic RNN is __________.

C. Overfitting

D. Large training parameters

Q.2 Feedforward network cannot be used on sequence data because ____________.

A. You cannot pass hidden states to next input

B. Dependencies on previous inputs will not be considered

C. Of the varying length of input data

D. All the options

Ans : All the options

Q.3 Which of the following are the applications of RNN?

A. Time series prediction

B. Machine translation

C. Intent extraction

D. All the options

Ans : Intent extraction

Q.4 Error function in RNN is computed as _________________.

A. Summation of errors at individual time step

B. Sum of errors at the first few time steps

C. Weighted sum of error values at each time step

D. Error value only at the final time step

Ans : Summation of errors at individual time step

Q.5 Back propagation through time is computed as ______________.

A. Summation of derivatives of parameters on error function over the first few time steps

B. Summation of derivatives of parameters on error function across each time step

C. Product of derivatives of parameters on error function across each time step

D. Derivatives of parameters on error function at the final time step

Ans : Summation of derivatives of parameters on error function across each time step

Q.6 Which of the following holds the memory in RNNs?

A. Error function

B. Hidden state

C. Output state

D. Input state

Ans : Error function

Q.7 Which of the following is not an example of sequential data?

A. Time series data

B. Text data

C. Speech data

D. Image data

Ans : Image data

Q.8 The basic RNN fails when _______________________.

A. Input sequence is too small

B. Input is of fixed length

C. Input sequence is very long

D. There is temporal dependencies in the data

Ans : Input sequence is very long

Q.9 In which of the following, the vanishing gradient problem is profound? —

A. GRU

B. Basic RNN

C. LSTM

Ans : Basic RNN

Q.10 In the case of LSTM, which of the following is used to compute the output of a cell?

A. Hidden state

B. Cell state

C. Input state

D. Reset state

Ans : Hidden state

Q.11 Which of the following gates (in LSTM) decides on keeping relevant features from the current input?

A. Input gate

B. Forget gate

C. Output gate

D. Update gate

Ans : Update gate

Q.12 Which of the following is an additional feature of LSTM when compared to basic RNN?

A. Hidden state

B. Cell state

C. Output state

D. Input State

Ans : Cell state

Q.13 Which of the following is not a feature of LSTM?

A. Hidden state

B. Reset gate

C. Update gate

D. Cell state

Ans : Hidden state

Q.14 GRU has more parameters to learn when compared to LSTM.

A. True

B. False

Ans : False

Q.15 LSTM has a large number of parameters to learn when compared to basic RNN.

A. True

B. False

Ans : True

Q.16 Which of the following is not the feature of GRU?

A. Hidden state

B. Reset gate

C. Update gate

D. Cell state

Ans : Cell state

Q.17 The reason feed forward network cannot be used on sequence data is _____________.

A. Dependencies on previous inputs will not be considered

B. Cannot pass hidden states to next input

C. Varying length of input data

D. All the options

Ans : Varying length of input data

Q.18 Which of the following is the component of GRU?

A. Forget gate

B. Reset gate

C. Input gate

D. Cell state

Ans : Reset gate

Q.19 In the case of LSTM, which of the following is used to compute the output of a cell?

A. Cell state

B. Hidden state

C. Input state

D. Reset state

Ans : Cell state

Q.20 Which of the following gates in LSTM decides on eliminating irrelevant features from previous information?

A. Input Gate

B. Update gate

C. Forget gate

D. Reset gate

Ans : Forget gate

Q.21 Which of the following gates in LSTM decides on keeping relevant features from the current input?

A. Output gate

B. Forget gate

C. Update gate

D. Input gate

Ans : Input gate

Q.22 Which of the following considers the information from previous timestep, current ones, as well as future timesteps?

A. GRU

B. LSTM

C. RNN

D. Bidirectional RNN

Ans : Bidirectional RNN

Q.23 What is meant by sequence data?

A. The data with a temporal relationship between them

B. The data with no relationship with other data

C. The data with a spacial relationship between them

D. Data arranged in a sequence

Ans : The data with a temporal relationship between them

Q.24 What is the entity that is carried from one timestep to next in RNN?

A. Concatenated inputs from previous timesteps

B. Final output

C. Previous timestep input

D. Hidden state

Ans : Hidden state

Q.25 De-noising and Contractive are examples of ______________

A. Shallow Neural Networks

B. Autoencoders

C. Convolution Neural Networks

D. Recurrent Neural Networks

Ans : Autoencoders

Q.26 How do RNTS interpret words?

A. One Hot Encoding

B. Lower Case Versions

C. Word Frequencies

D. Vector Representations

Ans : Vector Representations

Q.27 Autoencoders are trained using _______________.

A. Feed Forward

B. Reconstruction

C. Back Propagation

D. They do not require Training

Ans : Back Propagation

Q.28 The rate at which cost changes with respect to weight or bias is called __________________.

A. Derivative

C. Loss

D. Rate of Change

Q.29 A _________________ matches or surpasses the output of an individual neuron to a visual stimuli.

A. Max Pooling

C. Cost

D. Convolution

Ans : Convolution

Q.30 Autoencoders cannot be used for Dimensionality Reduction.

A. True

B. False

Ans : False

Sequence Models Interview Question- – 2