Create Machine Learning Models in Microsoft Azure Quiz Answers + Review

In this article, I’m simply sharing my thoughts and review of the Create Machine Learning Models in Microsoft Azure course along with all the quiz answers.

Just finished the course on Coursera? You’re in the right place! In this post, I’ll give you a quick review based on my experience and share all the quiz answers to help you move through the course with confidence.

Whether you’re brushing up on your machine learning skills, exploring Azure’s powerful tools, or working toward the DP-100 certification, this course is a solid step forward — and I’ve got your back with everything you need to pass!

Test Prep Quiz Answers / week 1

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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Test Prep Quiz Answers / week 2

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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Test Prep Quiz Answers / week 3

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

You might also like: Intro to Computers and OS Security Quiz Answers + Review

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My Review: Create Machine Learning Models in Microsoft Azure

I recently completed the course and found it to be an incredibly practical and well-structured introduction to machine learning within the Azure ecosystem. While some prior knowledge of Python or machine learning is helpful, the course does a solid job of walking through key concepts while quickly transitioning into real-world application. The use of Jupyter notebooks adds an interactive edge to the learning experience, letting you apply what you learn immediately.

The course balances foundational knowledge and hands-on training, making it a great fit for both aspiring data scientists and professionals aiming to integrate ML into Azure-based projects. Whether you’re working toward the DP-100 certification or just want to explore Azure ML, this course sets you up with the tools and confidence to get started.