AI-900 Day 8 Fundamentals of Machine Learning in Azure

Front

What is the primary purpose of Azure Machine Learning?

Back

To build, train, and deploy machine learning models

Answer explanation

The primary purpose of Azure Machine Learning is to build, train, and deploy machine learning models, enabling users to create intelligent applications and automate processes effectively.

Front

Which of the following is a key component of Azure Machine Learning?

• Azure Functions
• Azure Blob Storage
• Azure Machine Learning Studio
• Azure SQL Database

Back

Azure Machine Learning Studio

Answer explanation

Azure Machine Learning Studio is a key component that provides a web-based interface for building, training, and deploying machine learning models, making it essential for users working with Azure Machine Learning.

Front

What is the first step in the data preparation process?

Back

Data collection

Answer explanation

The first step in the data preparation process is data collection. This involves gathering the necessary data from various sources before any cleaning, transformation, or splitting can occur.

Front

Which technique is used to handle missing data in a dataset?

Back

Data imputation

Answer explanation

Data imputation is the technique used to fill in missing values in a dataset, making it essential for data analysis. Other options like data augmentation, normalization, and encryption serve different purposes.

Front

What is the purpose of data normalization?

Back

To scale data to a standard range

Answer explanation

The purpose of data normalization is to scale data to a standard range, which helps improve the performance of machine learning algorithms by ensuring that no single feature dominates due to its scale.

Front

Common metric for evaluating classification models: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Accuracy, R-squared

Back

Accuracy

Answer explanation

Accuracy is a common metric for evaluating classification models, as it measures the proportion of correctly classified instances. In contrast, MAE, RMSE, and R-squared are metrics used for regression models.

Front

What is overfitting in machine learning?

Back

When a model performs well on training data but poorly on new data

Answer explanation

Overfitting occurs when a model learns the training data too well, capturing noise and details that do not generalize to new data. This results in high accuracy on training data but poor performance on unseen data.

Front

Which of the following is a method to prevent overfitting?

Back

Using regularization techniques

Answer explanation

Using regularization techniques helps to prevent overfitting by adding a penalty for larger coefficients in the model, thus promoting simpler models that generalize better to unseen data.

Front

What is the purpose of a validation set in model training?

Back

To tune model parameters

Answer explanation

The validation set is used to tune model parameters by evaluating the model's performance on unseen data during training. This helps prevent overfitting and ensures the model generalizes well to new data.

Front

Common method for deploying machine learning models in Azure? Options: Azure Logic Apps, Azure Kubernetes Service, Azure DevOps, Azure Virtual Machines

Back

Azure Kubernetes Service

Answer explanation

Azure Kubernetes Service (AKS) is a common method for deploying machine learning models in Azure, as it provides a scalable and managed environment for containerized applications, making it ideal for ML workloads.