In this post, I’m sharing a quick review of the Microsoft Azure Machine Learning for Data Scientists course, along with key insights to help you prepare for the DP-100 certification exam.
Just finished this second course in the Microsoft Azure Data Scientist Associate Professional Certificate? The DP-100 exam is your next big step, and this course is designed to help you understand how to build, train, and deploy machine learning models at scale using Azure ML. It covers automation, no-code solutions, and cloud-based model management. If you’re aiming to master the Azure Machine Learning platform, this course is packed with practical lessons and real-world tools to assess your readiness — and I’ve got the review to help you prep smarter.
Microsoft Azure Machine Learning for Data Scientists
Table of Contents
Test Prep Quiz Answers / Week 1
Question 1)
A hospital wants to categorize patients that are pregnant as low-risk or high-risk regarding complications based on data like patient age and known medical conditions. What kind of machine learning model should the hospital use?
- Regression
- Classification
- Time series forecasting
Question 2)
Which of the following are machine learning models? Select all that apply.
- Time series forecasting
- Regression
- Polarization
Question 3)
A meteorological institute wants to predict, based on data from the past, how much it will rain next Sunday. What machine learning model is the best fit for this case?
- Time series forecasting
- Classification
- Regression
Question 4)
A toy company wants to predict the daily demand in order to assure that they have the necessary stock to honour all orders. What machine learning model can be used in this case?
- Classification
- Regression
- Clustering
Question 5)
Azure Machine Learning includes an automated machine learning capability that leverages the scalability of cloud compute to automatically try multiple pre-processing techniques and model-training algorithms in parallel to find the best performing supervised machine learning model for your data.
- True
- False
Question 6)
A bike rental company can use historic data to train a model that predicts daily rental demand in order to make sure sufficient staff and cycles are available.
- True
- False
Question 7)
What setting should you configure if you want to end the experiment if the model achieves a certain score or less on normalized root mean squared error metric?
- Blocked algorithms
- Metric score threshold
- Training compute target
Test Prep Quiz Answers / Week 2
Question 1)
What features and capabilities are available in Azure Machine Learning? Select all that apply.
- Train models
- Monitor usage of used services
- Prepare data
- Publish predictive services
Question 2)
After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to predict label values based on its features.
- True
- False
Question 3)
What type of compute resources can be created in Azure Machine Learning Studio?
- Compute instances
- Compute clusters
- Attached compute
- Inference clusters
- Spot clusters
Question 4)
You are creating a training pipeline for a regression model and you want to make sure that the dataset is complete, otherwise you need to perform various operations to fix the data. Which module should you add to the pipeline?
- Select columns in a dataset
- Clean missing data
- Normalize data
Question 5)
You are creating a training pipeline for a regression model and your dataset contains hundreds of columns. For a particular part of your model, you want to use data only from some specific columns. Which module should you add to the pipeline?
- Select columns in a dataset
- Clean missing data
- Normalize data
Question 6)
Which of the following scenarios can be resolved by using a regression model?
- Predict yearly income of customers based on their occupation, age, education etc.
- Predict selling price of a car using data like engine size, mileage, number of seats etc.
- Determine if patients with some pre-existing conditions are more likely to suffer from diabetes
- Predict daily rental demand of bicycles by using historic data.
Question 7)
You created a machine learning model and trained it. Now you want to run the model to predict data. Which compute target should you use?
- Compute Instances
- Compute Clusters
- Inference Clusters
Test Prep Quiz Answers / Week 3
Question 1)
Which metric presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions?
- Accuracy
- F1 Score
- Recall
- Precision
Question 2)
You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model’s performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.6. What can you conclude about the model?
- The model can explain 60% of the variance between true and predicted labels.
- The model performs better than random guessing
- The model predicts accurately for 40% of cases
Question 3)
Which metric presents the fraction of positives cases correctly identified?
- F1 Score
- Accuracy
- Precision
- Recall
Question 4)
Which of the following scenarios can be resolved by applying classification models?
- A toy company wanting to determine which clients are inclined to buy a specific toy.
- A company who wants to predict the churn rate of their subscribers for next month.
- A bank wanting to determine if a specific set of clients are eligible for taking a loan.
Question 5)
Which of the following are models that help predict between two or several categories?
Select all that apply.
- Multi-class neural network
- Two-class decision forest
- Two-class logistic regression
- Linear Regression
Question 6)
True or False?
Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.
- True
- False
Question 7)
You are using Azure Machine Learning designer to create a training pipeline for a binary classification model.At some point, you want to separate the data into training and testing sets. Which model should you add to the pipeline?
- Select columns in dataset
- Split data
- Join data
Test Prep Quiz Answers / Week 4
Question 1)
Which of the following is a clustering algorithm?
- Two-Class Neural Network
- Two-Class Logistic Regression
- K-Means
Question 2)
What is the purpose of a clustering model?
- Answers simple two-choice questions
- Makes forecasts by estimating the relationship between values
- Separates similar data points into intuitive groups
Question 3)
Which of the following scenarios can be resolved by applying clustering modules/algorithms?
Select all that apply.
- A radio company that wants to apply tags (like rock, pop, R&B etc) to songs or artists.
- A bike rental company that wants to predict the number of customers for the next day so that it will assure the necessary staff and cycles.
- A social media company that wants to group similar users based on their posts.
Question 4)
When evaluating a clustering model, what metrics can you visualize in the Evaluate results section?
Select all that apply.
- Average distance to other center
- Average distance to cluster center
- Number of points
- Maximal distance to cluster center
Question 5)
You are building an Azure Machine learning pipeline that involves a clustering module. You need to prepare the data and change some of the numeric values from the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
Which module should you apply?
- Split data
- Normalize Data
- Edit metadata
Question 6)
True or False?
Clustering is an example of supervised machine learning, in which you train a model to separate items into clusters based purely on their characteristics or features.
- True
- False
Question 7)
A Hospital Care chain wants to open a series of Emergency-Care wards within a region. The chain knows the location of all the maximum accident-prone areas in the region. They have to decide the number of the Emergency Units to be opened and the location of these Emergency Units, so that all the accident-prone areas are covered in the vicinity of these Emergency Units.
Which type of machine learning model is best to be applied in this scenario?
- Clustering
- Regression
- Classification
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My Review
I recently completed the “Microsoft Azure Machine Learning for Data Scientists” course on Coursera, and it’s a solid, hands-on learning experience for anyone preparing for the DP-100 certification. This 4-module course dives into using Azure Machine Learning to streamline the full machine learning lifecycle—from data ingestion and model training to deployment and monitoring—without requiring you to write any code.
What I really appreciated were the automated ML features, intuitive workflows, and integration with tools like Azure Databricks. The course builds on your Python and ML framework knowledge (like Scikit-learn, PyTorch, and TensorFlow), showing how to scale those skills in a cloud environment.
It not only strengthens your cloud-based ML skills but also reinforces the concepts Microsoft tests on the DP-100 exam. If you’re serious about earning your Azure Data Scientist Associate certification, this course is a great checkpoint to test your readiness and grow your confidence.