Azure DP 100: Mastering Data Science on Azure

Mastering Data Science on Azure is a comprehensive certification that helps you unlock the full potential of Microsoft Azure's data science capabilities. This certification is designed for data scientists, analysts, and engineers who want to work with Azure's data science tools and services.

To get started, you'll need to have a solid understanding of Azure's data science services, including Azure Databricks, Azure Machine Learning, and Azure Synapse Analytics. These services are the backbone of Azure's data science ecosystem and are used to build, deploy, and manage data science models.

The Azure DP 100 certification covers a wide range of topics, including data ingestion, processing, and storage, as well as machine learning and model deployment. You'll also learn how to use Azure's data science tools to work with large datasets and build scalable data science pipelines.

With the Azure DP 100 certification, you'll be able to work with Azure's data science services to build data-driven solutions that drive business value and improve decision-making.

The exam format for Azure DP 100 is straightforward.

You'll have 120 minutes to complete the exam, which is a decent amount of time to showcase your skills.

The exam is valid for 1 year, or 12 months, so you don't have to worry about rushing through it.

To pass, you'll need to score well, but the exact passing marks or score aren't specified.

We offer two full-length mock exams with over 110 unique Microsoft Azure DP-100 certification exam practice questions.

Our training videos cover all certification exam objectives and total over 5 hours. These videos are designed to be easy to understand, even for the most complex concepts.

Our support team consists of Azure experts who are ready to clarify any questions you may have.

Many of our customers have found our preparation tool to be very helpful in preparing for the DP-100 exam.

Designing and managing machine learning solutions in Azure requires careful planning and execution. To determine the appropriate compute specifications for a training workload, you need to consider factors such as the size and complexity of your dataset, as well as the type of model you're training.

To create a machine learning solution, you'll need to select a development approach that suits your needs, such as using a high-level API or low-level libraries. You'll also need to describe your model deployment requirements, including the infrastructure and resources needed to run your model in production.

Here are some key considerations for designing and managing machine learning solutions in Azure:

By following these best practices, you can ensure that your machine learning solution is scalable, efficient, and easy to manage.

Designing a solution is a crucial step in any project. To determine the appropriate compute specifications for a training workload, you need to consider the size and complexity of the data.

The compute specifications will depend on the specific requirements of the project. For example, if you're working with a large dataset, you'll need more powerful hardware to process it efficiently.

To build or train a model, you have several development approaches to choose from. These include using a visual interface, writing code from scratch, or using a combination of both.

When describing model deployment requirements, consider the infrastructure and resources needed to support the model in production. This includes the type of hardware, software, and storage required.

To get started, let's break down the key steps involved in designing a solution:

Managing data in an Azure Machine Learning workspace is a crucial step in the design and management process. You can select Azure Storage resources to store and manage your data.

To create and manage registries, you'll need to set up an Azure Machine Learning workspace. This will give you a centralized location to manage all your data and models.

You can register and maintain datastores, which will help you keep track of your data and ensure it's easily accessible. This is especially useful when working with large datasets.

To wrangle data interactively, you can access and wrangle data during interactive development. This allows you to explore your data in real-time and make adjustments as needed.

Here's a summary of the key steps to manage data in an Azure Machine Learning workspace: