COURSE 5: PREPARE FOR DP-100: DATA SCIENCE ON MICROSOFT AZURE EXAM
Module 3: Exam Preparation Course 2
MICROSOFT AZURE DATA SCIENTIST ASSOCIATE (DP-100) PROFESSIONAL CERTIFICATE
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INTRODUCTION – Exam Preparation Course 2
In this module, you will delve into a comprehensive review of Course 2 of the Microsoft Azure Data Scientist Associate specialization. This course builds on the foundational knowledge gained in Course 1, advancing your skills in data science and machine learning using Azure’s powerful tools and services. You will revisit key concepts such as advanced data preparation techniques, feature engineering, and model selection. The course also covers the use of Azure Machine Learning for automating the end-to-end machine learning lifecycle, from data ingestion and preprocessing to model training, evaluation, and deployment.
Additionally, the course introduces best practices for monitoring and managing machine learning models in production, ensuring their performance and reliability over time. This review will reinforce your understanding of these advanced topics and equip you with the practical skills needed to tackle real-world data science challenges using Azure.
Learning Objectives
- Outline the key points covered in the Microsoft Azure Data Scientist Associate specialization
- Recap on main topic in Course 2: Create no-code predictive models with Azure Machine Learning
- Assess knowledge and skills in the creating no-code predictive models with Azure Machine Learning
Quiz: Create no-code predictive models with Azure Machine Learning
1. What data values are influencing prediction models?
- Labels
- Identifiers
- Features (CORRECT)
- Dependent variables
Correct: Features are data values that influence prediction models.
2. Let’s suppose you want to create an AI system that can predict how many minutes late a flight will arrive based on the amount of snowfall at an airport. Which machine learning type should you use?
- Classification
- Regression (CORRECT)
- Clustering
Correct: Regression is a supervised machine learning technique used to predict numeric values.
3. Imagine you work for a government institution that wants to predict the sea level in meters for the following 10 years. Which type of machine learning should you use?
- Regression (CORRECT)
- Clustering
- Classification
Correct: Regression is a supervised machine learning technique used to predict numeric values.
4. Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Which two components can be dragged-and-dropped onto the canvas?
Select all options that apply.
- Pipeline
- Compute
- Dataset (CORRECT)
- Module (CORRECT)
Correct: The two components that can be dragged-and-dropped onto the canvas are datasets and modules.
Correct: The two components that can be dragged-and-dropped onto the canvas are datasets and modules.
5. True or False?
When working in Azure Machine Learning designer, it is possible to save your progress as a pipeline draft.
- True (CORRECT)
- False
Correct: Azure Machine Learning designer offers the possibility to save progress as a pipeline draft.
6. You can use AI systems to predict whether a student will complete a university course. Which machine learning type enables you to do that?
- Classification (CORRECT)
- Regression
- Clustering
Correct: Classification is a supervised machine learning technique used to predict categories or classes.
7. True or False?
Accuracy is always the primary metric used to measure a model’s performance. Is this true?
- True
- False (CORRECT)
Correct: There are different metrics that can be used to measure a model’s performance.
8. True or False?
Automated machine learning can automatically infer the training data from the use case provided.
- True
- False (CORRECT)
Correct: Automated machine learning cannot automatically infer the training data from the use case provided.
9. True or False?
Azure Machine Learning designer provides a drag-and-drop visual canvas to build, test, and deploy machine learning models.
- True (CORRECT)
- False
Correct: Azure Machine Learning provides a drag-and-drop visual canvas to build, test, and deploy machine learning models.
10. Fill in the blank.
__________ is a form of machine learning that has the capability to group similar items based on their features.
- Regression
- Clustering (CORRECT)
- Classification
Correct: Clustering is an unsupervised machine learning technique used to group similar entities based on their features.
11. In a machine learning algorithm, what method should you use to split data for training and evaluation?
- Randomly split the data into rows for training and columns for evaluation
- Use labels for training and features for evaluation
- Use features for training and labels for evaluation
- Randomly split the data into rows for training and rows for evaluation (CORRECT)
Correct: In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
12. Which of the following metrics is used to evaluate a classification model?
- Coefficient of determination (R2)
- Root mean squared error (RMSE)
- True positive rate (CORRECT)
- Mean absolute error (MAE)
Correct: The best metric to evaluate a classification model is by looking at the true positive rate.
13. Which module in the Azure Machine Learning designer should you use if you want to create a training dataset and a validation dataset from an existing dataset?
- Select columns in dataset
- Split data (CORRECT)
- Join data
- Add rows
Correct: Datasets can be split into training datasets and validation datasets by splitting the data.
14. Let’s suppose you are working on an AI application that should predict the weather. From the dataset you have, you want to pick temperature and pressure to train the model. Which machine learning task enables you to do that?
- Feature engineering
- Feature selection (CORRECT)
- Model training
Correct: Feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model.
15. Predicting whether someone uses a bicycle to travel to work based on the distance from home to work is a use case for?
- Clustering
- Regression
- Classification (CORRECT)
Correct: Classification is a supervised machine learning technique used to predict categories or classes.
16. You want to create a CRM application that uses AI to segment customers into different groups to support a marketing department. Which machine learning type should you use?
- Classification
- Clustering (CORRECT)
- Regression
Correct: Clustering is an unsupervised machine learning technique used to group similar entities based on their features.
17. True or False?
Azure Machine Learning designer supports custom JavaScript functions.
- True
- False (CORRECT)
Correct: Azure Machine Learning designer does not support custom JavaScript functions.
18. Predicting how many minutes it will take someone to run a race based on past race times is a use case for?
- Regression (CORRECT)
- Clustering
- Classification
Correct: Regression is a supervised machine learning technique used to predict numeric values.
CONCLUSION – Exam Preparation Course 2
By the end of this module, you will have a reinforced understanding of advanced data science and machine learning techniques using Azure, as covered in Course 2 of the Microsoft Azure Data Scientist Associate specialization. You will be equipped with practical skills in advanced data preparation, feature engineering, model selection, and the automation of the machine learning lifecycle using Azure Machine Learning.
Additionally, you will be knowledgeable about best practices for monitoring and managing models in production, ensuring their ongoing performance and reliability. This solid foundation will prepare you for tackling real-world data science challenges and advancing in your specialization journey.
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