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Create Machine Learning Models in Microsoft Azure Quiz Answers

 

WEEK 1 QUIZ ANSWERS

Knowledge check

Question 1)
Which Python libraries are used in machine learning and deep learning?
Select all options that apply.

• PyTorch
• Scikit-learn
• TensorFlow
• Matplotlib

Question 2)
What are the benefits of using the NumPy and Pandas libraries in your Python project?

• Offering simple and predictive data analysis
• Supplying machine learning and deep learning capabilities
• Simplifying analyzing and manipulating data
• Providing attractive data visualizations

Question 3)
Which Python library provides functionalities similar to excel?

• Pandas
• Matplotlib
• NumPy

Question 4)
Which Python library provides functionality comparable to mathematical tools such as MATLAB and R?

• Scikit-learn
• TensorFlow
• NumPy

Question 5)
What should you use if you want to run basic scripts in a web browser?

• Pandas
• NumPy
• Jupyter notebook

 

Knowledge check

Question 1)
In a regression model, what is a feature and what is a label?

• A feature is the variable to be predicted.
• A label is the variable that represents characteristics.
• A feature is the variable that represents characteristics.
• A label is the variable to be predicted.

Question 2)
When you want to train a regression model based on historical data, which are the two subsets into which you split the data sample?

• A validation dataset
• A performance dataset
• A confirmation dataset
• A training dataset

Question 3)
True or False? In machine learning, the difference between a predicted label value and the actual value is known as “the residuals”.

• True
• False

Question 4)
To randomly split the data between training and validation subsets, you can use the train_test_split function. In which python library can you find this function?

• Numpy
• Scikit-learn
• Pytorch
• Matplotlib

Question 5)
This evaluation metric yields a relative metric in which the smaller the value, the better the fit of the model. Which evaluation metric is described above?

• Coefficient of Determination (usually known as R-squared or R2)
• Mean Square Error (MSE)
• Root Mean Square Error (RMSE)

 

Test Prep Quiz Answers

Question 1)
How would a list and a NumPy array behave when they are multiplied by 3?

• Multiplying a NumPy array by 3 performs an element-wise calculation on the array, which sees the array stay the same size, but each element has been multiplied by 3.
• Multiplying an NumPy array by 3 creates a new array 3 times the length with the original sequence repeated 3 times.
• Multiplying a list by 3 creates a new list 3 times the length with the original sequence repeated 3 times.
• Multiplying a list by 3 performs an element-wise calculation on the list, which sees the list stay the same size, but each element has been multiplied by 3.

Question 2)
If you have a NumPy array with the shape (2,35), what does this tell you about the elements in the array?

• The array contains 35 elements, all with the value 2.
• The array is two dimensional, consisting of two arrays with 35 elements each.
• The array contains 2 elements with the values of 2 and 35.

Question 3)
Suppose you have a Pandas DataFrame named df_sales containing daily sales data. The DataFrame has the following columns: year, month, day_of_month, sales_total. If you want to find the average sales_total value, which code should you use?

• mean(df_sales[‘sales_total’])
• df_sales[‘sales_total’].mean()
• df_sales[‘sales_total’].avg()

Question 4)
You work on a DataFrame containing data about daily ice cream sales. You use the corr method to compare the avg_temp and units_sold columns, and get a result of 0.95. What does this result indicate?

• The units_sold value is, on average, 95% of the avg_temp value.
• On the day with the maximum units_sold value, the avg_temp value was 0.95.
• Days with high avg_temp values tend to coincide with days that have high units_sold values.

Question 5)
This is a relative metric in which the higher the value, the better the fit of the model.

Which evaluation model is described?

• Mean Square Error (MSE)
• Root Mean Square Error (RMSE)
• Coefficient of Determination (known as R-squared or R2)

Question 6)
This evaluation metric yields an absolute metric in the same unit as the label.

Which metric is described?

• Coefficient of Determination (known as R-squared or R2)
• Root Mean Square Error (RMSE)
• Mean Square Error (MSE)

Question 7)
You’ve just created a model object using the LinearRegression class from the scikit-learn library.

What should you do next to train the model?

• Call the predict() method of the model object, specifying the training feature and label arrays.
• Call the score() method of the model object, specifying the training feature and test feature arrays.
• Call the fit() method of the model object, specifying the training feature and label arrays.

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WEEK 2 QUIZ ANSWERS

Knowledge check

Question 1)
True or False? Classification is an unsupervised machine learning technique.

• True
• False

Question 2)
Complete the statement:
Classification is a form of machine learning in which you train a model to predict […] of an item.

• The numeric value
• The feature
• The category

Question 3)
Which Python package contains the train_test_split function?

• Matplotlib
• Tensorflow
• Scikit-learn
• Numpy

Question 4)
How would you split your data for training and testing to ensure the model performs well?

• 30% training and 70% testing
• 50% training and 50% testing
• 70% training and 30% testing

Question 5)
For machine learning algorithms, how are parameters generally referred to as?

• Staticparameters
• Hyperparameters
• Superparameters

 

Knowledge check

Question 1)
True or False?
Clustering is an example of a supervised machine learning technique.

• True
• False

Question 2)
When working on a clustering model, if you want to measure how tightly the data points are grouped, what metric should you use?

• R-squared (R2)
• Receiver operating characteristic (ROC) curve
• F1 score
• Within cluster sum of squares (WCSS)

Question 3)
This clustering algorithm separates a dataset into clusters of equal variances, where the number of clusters is user-defined.
Which clustering algorithm is this?

• Logistic regression
• K-means
• Hierarchical

Question 4)
Select the correct steps that a basic K-means clustering algorithm consists of:

• Steps 2 and 3 (b & c here) are repeated until a stopping criteria is met.
• When the clusters stop changing, the algorithm has converged.
• A set of K centroids are specifically chosen.
• The means of each cluster is computed and the centroid is moved to the mean.
• Clusters are formed by assigning the data points to a random centroid.

Question 5)
Which of the following algorithms are considered to be clustering-type algorithms?

• K-Means
• Hierarchical
• Decision Tree

 

Test prep Quiz Answers

Question 1)
Which type of machine learning model can be trained using the Support Vector Machine algorithm?

• Regression
• Clustering
• Classification

Question 2)
When using the classification report from sklearn.metrics to evaluate the performance of your model, what does the F1-Score metric provide?

• An average metric that takes both precision and recall into account.
• Of the predictions the model made for this class, what proportion were correct.
• How many instances of this class are there in the test dataset.
• Out of all of the instances of this class in the test dataset, how many did the model identify.

Question 3)
The Precision and Recall metrics are derived from four possible prediction outcomes.
If the predicted label is 1, but the actual label is 0, what would the outcome be?

• False Negative
• True Negative
• True Positive
• False Positive

Question 4)
In multiclass classification, what are the two ways in which you can approach a problem?

• Rest minus One
• One vs One
• One and Rest
• One vs Rest

Question 5)
Hierarchical clustering creates clusters using two methods.
Which are those two methods?

• Distinctive
• Agglomerative
• Divisive
• Aggregational

Question 6)
To which kind of machine learning can the K-Means clustering algorithm be associated with?

• Unsupervised machine learning
• Supervised machine learning
• Reinforcement learning

Question 7)
You are using scikit-learn library to train a K-Means clustering model that groups observations into four clusters. How should you create the K-Means object?

• model = KMeans(n_clusters=4)
• model = Kmeans(n_init=4)
• model = Kmeans(max_iter=4)

 

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WEEK 3 QUIZ ANSWERS

Knowledge check

Question 1)
Complete the sentence:
Deep learning is an advanced form of machine learning that aims to[…]?

• Emulate the way the human brain learns.
• Make machines learn better and faster than humans.
• Help humans develop better learning skills.

Question 2)
In a deep learning model, the incoming nerve connections are emulated by numeric inputs which are typically identified as x.
Each x value is associated with some inputs. Which are those inputs?

• Speed
• Ratio
• Bias
• Weight

Question 3)
The training process of a deep neural network consists of multiple iterations.
What is the name of those iterations?

• Cycles
• Epochs
• Eras

Question 4)
During the training of a deep neural network, inside an epoch, in which step is the loss calculated?

• 1st
• 4th
• 2nd
• 3rd

Question 5)
True or False?
When training deep learning models, computers with GPUs perform better than computers with CPUs only.

• True
• False

 

Knowledge check

Question 1)
In which AI domain are deep learning models particularly useful?

• Text processing
• Natural language processing (NLP)
• Speech processing
• Image processing

Question 2)
How does a convolutional neural network typically work?

• By grouping objects based on similar features
• By comparing features and known labels to generate a prediction
• By extracting features from images and feeding them into a network to generate a prediction

Question 3)
A convolutional network consists of multiple layers.
Which are those layers?

• Scanning layers
• Pooling layers
• Flattening layers
• Fully connected layers
• Curving layers
• Convolution layers
• Dropping layers

Question 4)
Which convolutional network layer helps ensure that the model doesn’t become over-dependent on the training images?

• Fully connected layer
• Dropping layer
• Flattening layer

Question 5)
True or False?
A convolutional layer applies multiple filter kernels, where each filter produces a different feature map, and all of the feature maps are passed onto the next layer of the network.

• True
• False

 

Test prep Quiz Answers

Question 1)
You developed a deep neural network to train a classification model that predicts to which of the four classes an observation belongs based on 8 numeric features.
Which of the following statements is true about the network architecture?

• The network layer should contain four hidden layers
• The output layer should contain four nodes
• The input layer should contain four nodes

Question 2)
You are training a deep neural network. You configured the training process to use 35 epochs.
What effect does this have on the behavior of the model?

• The training data is split into 35 subsets, and each subset is passed through the network.
• The entire training dataset is passed through the network 35 times.
• The first 35 rows of data are used to train the model, and the remaining rows are used to validate it.

Question 3)
When creating a deep neural network, if you increase the Learning Rate parameter, what effect will this setting have?

• More records are included in each batch passed through the network.
• Larger adjustments are made to weight values during backpropagation.
• More hidden layers are added to the network.

Question 4)
When creating a convolutional neural network, if you want to reduce the size of the feature maps that are generated by a convolutional layer, what should you do?

• Add a pooling layer after the convolutional layer.
• Reduce the size of the filter kernel used in the convolutional layer.
• Increase the number of filters in the convolutional layer.

Question 5)
True or False?
The feature extraction layers apply convolutional filters and pooling to emphasize edges, corners, and other patterns in the images that can be used to differentiate them, and in theory should work for any set of images with the same dimensions as the input layer of the network.

• True
• False

Question 6)
This layer is a principal layer type that extracts important features in images. It works by applying a filter to images. What type of layer is this?

• Convulsion layer
• Flattening layer
• Pooling layer

Question 7)
After extracting feature values from images, this layer is used to reduce the number of feature values while retaining the key differentiating features that have been extracted.

• Convulsion layer
• Pooling layer
• Flattening layer