CAIP CERTNEXUS EXAM QUESTIONS
WITH REVIEWED 100% CORRECT
DETAILED ANSWERS
What are two situations that SVMs are a better choice than other classification and
regression algorithms? - Answer-When the data contains outliers and when there are
many dimensions
Which of the following kernels are most prone to overfitting the data? (Select two.)
Linear
Gaussian
Sigmoid
Polynomial - Answer-Gaussian radial basis function (RBF) may be prone to overfitting.
A high polynomial degree may lead to polynomial overfitting.
In which of the following might a sigmoid kernel be used?
Image classification
Time series
Natural language processing (NLP)
Clustering - Answer-Image Classification - A sigmoid kernel with SVMs is very similar to
a neural network and can be used for image classification and object detection.
Sigmoid kernels would not be used in a time series or clustering
How can you select which SVM kernel has the best skill for a particular dataset?
Use the kernel that is automatically chosen for you
Use a grid search function
Use a score function
Always use Gaussian - Answer-The grid search function allows all selected kernels and
hyper parameters to be tried in combination with the best combination reported by the
function
While a score function can provide a comparative value for a kernel, each would have to
be tested individually, which would not be efficient.
Which logical operation cannot be modeled using a simple perceptron?
OR
AND
XOR
,NOT - Answer-The XOR operation is not linearly separable and can't be modeled by a
simple perceptron.
How are the neurons in a multi-layer perceptron (MLP) hidden layer connected?
Each neuron in a hidden layer is connected to all neurons in the output layer.
Selected neurons in a hidden layer are connected to selected neurons in the output
layer.
Selected neurons in a hidden layer are connected to all neurons in the output layer.
Each neuron in a hidden layer is connected to selected neurons in the output layer. -
Answer-Each neuron in a hidden layer is connected to all neurons in the output layer. -
these are referred to as fully connected layers
Selected neurons in a hidden layer are connected to selected neurons in the output
layer. - not the case
Selected neurons in a hidden layer are connected to all neurons in the output layer. -
This is not the case in hidden layers in MLPs.
Hidden layer(s) can - Answer-- Have arbitrary number of nodes/units
- Have arbitrary number of links from input nodes and to output nodes (or to next hidden
layer)
- There can be multiple hidden layers
Which of the following methods is used to train a multi-layer perceptron (MLP) network?
Error calculations start at the end of the network and then work backwards.
The activation function removes bad predictions.
Error calculations start at the first layer of the network and then are propagated to the
next layer.
Neurons that make incorrect predictions have their weight increased. - Answer-Error
calculations start at the end of the network and then work backwards. - This is called
backpropagation. Starting from the last hidden layer, the neurons that would have led to
a correct prediction have their weights increased. This repeats for each layer moving
backwards through the network until the input layer is reached.
Error calculations start at the first layer of the network and then are propagated to the
next layer. - this is not the way error calculations are propagated
Which of the following concepts refers to actionable intelligence?
Wisdom
Data
Knowledge
Information - Answer-Knowledge
Knowledge is actionable intelligence because it can reveal some action that must be
taken.
You have a dataset of customer information, such as a customer's location, spending
habits, product reviews, and so forth. While you don't have anything specific to predict,
, you want to engage in customer segmentation so that customers with similarities are
considered a unified audience in your targeted marketing campaigns. Which type of
machine learning outcome is most appropriate for this situation?
Dimensionality reduction
Classification
Regression
Clustering - Answer-Clustering
Clustering is a type of unsupervised learning that groups together data points with
similarities, and is therefore an excellent outcome when the goal is customer
segmentation.
Which of the following describes the relationship between a machine learning model
and a machine learning algorithm?
A machine learning model represents the input data before it is fed into a machine
learning algorithm.
A machine learning model generates a machine learning algorithm through training.
A machine learning model is the sum of multiple machine learning algorithms.
A machine learning model is created by applying a machine learning algorithm to input
data. - Answer-A machine learning model is created by applying a machine learning
algorithm to input data.
The algorithm is a set of rules upon which the model is built. The model uses these
rules to estimate something about the world through available data. Think of an
algorithm as a blueprint or recipe, and the model as the finished building when given
resources or the finished meal when given ingredients.
What does it mean for a machine learning model to be "stochastic"? - Answer-The
same input can produce different outputs over multiple training sessions.
Which of the following is a Python tool that provides a frontend environment to the
TensorFlow deep learning library?
SciPy
Apache Spark MLlib
Keras
PyTorch - Answer-Keras
Keras is a high-level interface into TensorFlow, and is particularly useful for beginners
new to deep learning.
SciPy is a component of the Python data science stack that provides mathematical
functions, not a frontend to TensorFlow.
PyTorch is a deep learning library that is similar to TensorFlow, but it is not a frontend to
TensorFlow.
Apache Spark MLlib is a framework for machine learning that leverages clustering
computing, not a frontend to TensorFlow.
Which of the following cloud services is used as a teaching tool for reinforcement
learning?
AWS DeepRacer
WITH REVIEWED 100% CORRECT
DETAILED ANSWERS
What are two situations that SVMs are a better choice than other classification and
regression algorithms? - Answer-When the data contains outliers and when there are
many dimensions
Which of the following kernels are most prone to overfitting the data? (Select two.)
Linear
Gaussian
Sigmoid
Polynomial - Answer-Gaussian radial basis function (RBF) may be prone to overfitting.
A high polynomial degree may lead to polynomial overfitting.
In which of the following might a sigmoid kernel be used?
Image classification
Time series
Natural language processing (NLP)
Clustering - Answer-Image Classification - A sigmoid kernel with SVMs is very similar to
a neural network and can be used for image classification and object detection.
Sigmoid kernels would not be used in a time series or clustering
How can you select which SVM kernel has the best skill for a particular dataset?
Use the kernel that is automatically chosen for you
Use a grid search function
Use a score function
Always use Gaussian - Answer-The grid search function allows all selected kernels and
hyper parameters to be tried in combination with the best combination reported by the
function
While a score function can provide a comparative value for a kernel, each would have to
be tested individually, which would not be efficient.
Which logical operation cannot be modeled using a simple perceptron?
OR
AND
XOR
,NOT - Answer-The XOR operation is not linearly separable and can't be modeled by a
simple perceptron.
How are the neurons in a multi-layer perceptron (MLP) hidden layer connected?
Each neuron in a hidden layer is connected to all neurons in the output layer.
Selected neurons in a hidden layer are connected to selected neurons in the output
layer.
Selected neurons in a hidden layer are connected to all neurons in the output layer.
Each neuron in a hidden layer is connected to selected neurons in the output layer. -
Answer-Each neuron in a hidden layer is connected to all neurons in the output layer. -
these are referred to as fully connected layers
Selected neurons in a hidden layer are connected to selected neurons in the output
layer. - not the case
Selected neurons in a hidden layer are connected to all neurons in the output layer. -
This is not the case in hidden layers in MLPs.
Hidden layer(s) can - Answer-- Have arbitrary number of nodes/units
- Have arbitrary number of links from input nodes and to output nodes (or to next hidden
layer)
- There can be multiple hidden layers
Which of the following methods is used to train a multi-layer perceptron (MLP) network?
Error calculations start at the end of the network and then work backwards.
The activation function removes bad predictions.
Error calculations start at the first layer of the network and then are propagated to the
next layer.
Neurons that make incorrect predictions have their weight increased. - Answer-Error
calculations start at the end of the network and then work backwards. - This is called
backpropagation. Starting from the last hidden layer, the neurons that would have led to
a correct prediction have their weights increased. This repeats for each layer moving
backwards through the network until the input layer is reached.
Error calculations start at the first layer of the network and then are propagated to the
next layer. - this is not the way error calculations are propagated
Which of the following concepts refers to actionable intelligence?
Wisdom
Data
Knowledge
Information - Answer-Knowledge
Knowledge is actionable intelligence because it can reveal some action that must be
taken.
You have a dataset of customer information, such as a customer's location, spending
habits, product reviews, and so forth. While you don't have anything specific to predict,
, you want to engage in customer segmentation so that customers with similarities are
considered a unified audience in your targeted marketing campaigns. Which type of
machine learning outcome is most appropriate for this situation?
Dimensionality reduction
Classification
Regression
Clustering - Answer-Clustering
Clustering is a type of unsupervised learning that groups together data points with
similarities, and is therefore an excellent outcome when the goal is customer
segmentation.
Which of the following describes the relationship between a machine learning model
and a machine learning algorithm?
A machine learning model represents the input data before it is fed into a machine
learning algorithm.
A machine learning model generates a machine learning algorithm through training.
A machine learning model is the sum of multiple machine learning algorithms.
A machine learning model is created by applying a machine learning algorithm to input
data. - Answer-A machine learning model is created by applying a machine learning
algorithm to input data.
The algorithm is a set of rules upon which the model is built. The model uses these
rules to estimate something about the world through available data. Think of an
algorithm as a blueprint or recipe, and the model as the finished building when given
resources or the finished meal when given ingredients.
What does it mean for a machine learning model to be "stochastic"? - Answer-The
same input can produce different outputs over multiple training sessions.
Which of the following is a Python tool that provides a frontend environment to the
TensorFlow deep learning library?
SciPy
Apache Spark MLlib
Keras
PyTorch - Answer-Keras
Keras is a high-level interface into TensorFlow, and is particularly useful for beginners
new to deep learning.
SciPy is a component of the Python data science stack that provides mathematical
functions, not a frontend to TensorFlow.
PyTorch is a deep learning library that is similar to TensorFlow, but it is not a frontend to
TensorFlow.
Apache Spark MLlib is a framework for machine learning that leverages clustering
computing, not a frontend to TensorFlow.
Which of the following cloud services is used as a teaching tool for reinforcement
learning?
AWS DeepRacer
