Azure 100 Exam and Certification Guide

The Azure 100 exam is a comprehensive assessment of your skills in designing and implementing Microsoft Azure solutions. It's a challenging but rewarding certification that can boost your career prospects and open up new opportunities.

To pass the exam, you'll need to demonstrate expertise in a wide range of Azure services, including virtual machines, storage, networking, and security. You'll also need to show a solid understanding of Azure's pricing and billing model.

The exam consists of multiple-choice questions, case studies, and hands-on exercises that test your ability to design and implement Azure solutions. You'll have 100 minutes to complete the exam, and you'll need to answer at least 40 questions correctly to pass.

Microsoft offers a range of study resources to help you prepare for the exam, including online courses, tutorials, and practice exams.

The Azure 100 certification is role-based, testing your knowledge of designing and deploying a data science solution in an Azure environment using Azure Machine Learning as a potential Data Scientist.

A good understanding of machine learning and Python is essential for this certification, as it's focused on the technical usage of Azure Machine Learning Studio and Azure ML SDK.

The certification is valid for a year, after which re-certification is required.

To become an Azure Administrator, you'll need to prepare for the role by learning about cloud services that span storage, networking, and compute capabilities.

This preparation will help you understand the full IT lifecycle, including provisioning, sizing, monitoring, and adjusting cloud applications.

The AZ-100 certification training is designed for Azure Administrators, who manage cloud services and take end-user requests for new cloud applications.

You'll learn to recommend services for optimal performance and scale, as well as communicate and coordinate with vendors.

As you become more proficient, you'll learn to use the Azure Portal, PowerShell, and the Command Line Interface to manage cloud services.

This training will help you develop the skills and knowledge needed to pass the AZ-100 certification exam and become a certified Azure Administrator.

Azure Databricks is gaining momentum in the Data Science domain, with a focus on attaching it to the Azure Workspace, creating clusters, and processing data using PySpark.

Databricks is a powerful tool for data analytics and AI needs, and customers using it can benefit from Cobalt 100 VMs, which provide a significant boost in performance and cost-efficiency.

These VMs, built with Arm architecture, are designed to power scale-out, cloud-native solutions and deliver up to 40% better performance than the previous generation of Arm-based VMs.

Customers can leverage these benefits by selecting Cobalt 100 VMs as cluster nodes for their ETL jobs and batch processing on Azure Databricks.

The general availability of Azure Cobalt 100 VMs marks an important milestone in the partnership between Microsoft and the company, showcasing their joint efforts to provide best-in-class experiences for Azure Databricks customers.

Ready to explore the capabilities of Azure Cobalt 100 VMs? You can get started with an Azure Databricks free trial today.

With our Microsoft Azure Infrastructure and Deployment training, you'll gain in-depth knowledge in managing Azure subscriptions and resources.

To manage Azure subscriptions and billing, and implement Azure policies, you'll need to know how to track and estimate service usage and related costs.

You'll learn how to grant appropriate access to Azure AD users, groups, and services through Role-based access control (RBAC).

To use Azure Monitor to configure Azure alerts and review the Azure Activity Log, you'll need to understand how cloud resources are managed in Azure through user and group accounts.

Deploying resources with ARM templates and organizing Azure resources is a key part of this training.

You'll learn how to optimize your use of Azure tools like the Azure portal, Azure PowerShell, Cloud Shell, and the Azure CLI.

Azure Administrators manage the cloud services that span storage, networking, and compute cloud capabilities.

To become an Azure Administrator, you'll need familiarity with operating systems, virtualization, and cloud infrastructure.

Understanding of storage structures and networking is also a prerequisite for this role.

The Azure 100 features are designed to make working with complex data workloads a breeze. With up to 50% better price-performance compared to previous generations, Cobalt 100 VMs enable Databricks customers to benefit from superior performance and lower operating costs.

You can create and train models in the pipeline using classes like Run, RunConfiguration, and ScriptRunConfig, which help in understanding the basics of model development. The Azure ML SDK also allows you to create a pipeline for models and publish the best run and fitted model to a real-time system.

Here are some of the key features of the Azure 100:

Authentication is also a key feature of the Azure 100, which includes different forms of REST point authentication used for Azure Container Instances and Azure Kubernetes Services. This is used to create a secure environment for data processing and analytics.

In Part 2 of our Azure 100 Features series, we're diving into the world of Azure Machine Learning (Azure ML). As a developer, I've found that Azure ML is a powerful tool for building, training, and deploying machine learning models at scale.

Azure ML SDK is a Python library that allows you to work with Azure environment using any IDE. I personally used VSCode for hands-on experience, but Spyder can also be a good option.

Resource Handling is a crucial aspect of Azure ML, and the SDK provides a range of classes for defining and managing resources. These include Workspace, Experiment, Compute Instances, clusters, and compute target classes. You can expect to see code-based questions on creating these resources, attaching a compute (Azure Databricks), and batch inferencing.

Here are some key points to focus on when it comes to Resource Handling in Azure ML SDK:

Creating and training models in a pipeline is another key aspect of Azure ML. This involves using classes like Run, RunConfiguration, and ScriptRunConfig to define and execute machine learning workflows. You can expect to see questions on creating pipelines, publishing models to real-time systems, and using classes like ParallelRunStep.

Authentication is also an important aspect of Azure ML, and you can expect to see questions on using REST point authentication for Azure Container Instances and Azure Kubernetes Services. This includes key and token-based processes, where the key is retrieved from the Webservice method of the authenticated session.

Finally, model interpretability is a crucial aspect of machine learning, and Azure ML provides a range of tools and techniques for understanding and explaining model behavior. This includes global and local explainers, which provide insights into how models are making predictions.

Passing the DP-100 exam comes with several benefits. You earn the Microsoft Certified: Azure Data Scientist Associate certification, which is valid for a full calendar year.

This certification can give you a competitive edge in your career, helping you get promotions, raises, or even new jobs. In fact, having this certificate helped me build the confidence to teach machine learning on Azure to others and represent it in the community.

The certification also allows you to re-test and renew it for free using your web browser, which is a great perk. This means you can refresh your skills and stay up-to-date with the latest developments in Azure without any additional cost.

Microsoft offers a DP-100 learning path on their Docs platform, which has four modules, but modules 3 and 4 cover around 80% of the questions.

The Pluralsight learning path for DP-100 contains roughly 8 hours of content, but more content is being added at the time of writing.

Coursera partnered with Microsoft to offer a DP-100 test prep specialization, which I found to be very helpful and even offered a discount on the actual exam by completing it.

The Coursera specialization is a good option, especially considering the discount on the exam.

Udemy has a number of resources, but the best one I encountered was the DP-100 Microsoft Azure Data Scientist Complete Exam Prep course by Scott Duffy, which was fine but not in too much depth.

Cloud Academy offers a DP-100 exam prep learning path with interactive lab environments, giving it a very hands-on approach.

Here are some popular courses for DP-100 exam prep:

The course outline for Azure 100 features is a crucial part of preparing for the DP-100 exam. The exam focuses on designing and deploying a data science solution in an Azure environment using Azure Machine Learning as a potential Data Scientist.

Creating Virtual Machines using ARM Templates is a key aspect of Azure 100 features. This is a fundamental concept that you'll need to understand to pass the exam.

Creating Azure Virtual Networks is another important topic in the Azure 100 features course outline. This is where you'll learn how to create and manage virtual networks in Azure.

Here is a summary of the key topics covered in the Azure 100 features course outline:

The Azure Cobalt 100 VMs offer significant price-performance and efficiency benefits, with up to 50% better price-performance compared to previous generations.

This means that Databricks customers can enjoy superior performance and lower operating costs, which is particularly valuable for Azure Databricks workloads.

With up to 1.4x CPU performance, Azure Databricks users can process more data faster, and with up to 2x performance on web servers and in-memory cache applications, they can also reduce latency for business intelligence and analytics applications.

The energy-efficient Arm-based architecture of Cobalt 100 VMs reduces the power consumption of data operations, enabling Azure Databricks to optimize cloud infrastructure while delivering high performance.

Azure Kubernetes Service is a managed container orchestration service that's part of Azure. I learned this the hard way when I realized I should have played with it more during my prep work.

AKS is absolutely part of the exam, so make sure to familiarize yourself with its configuration options. If you're new to AKS, start by exploring its features and capabilities.

Token-based authentication is a key aspect of AKS, but I didn't get to work through this scenario during my prep work. Dynamic scaling configuration is another important feature to understand, but I didn't deal with it either.

Azure Container Instances and AKS are both compute services in Azure, but they serve different purposes. For more information on this, check out my article on compute management in Azure Machine Learning.

The DP-100 exam is a fun one to take, and it's a great way to gain more breadth and depth in machine learning on Azure.

Studying for the exam will give you a deeper understanding of individual machine learning algorithms, which will be incredibly helpful in the future.

I personally enjoyed the DP-100 exam, and it's now one of my favorite certification exams.

While it's not a guarantee of a new job or higher pay, the certification will give you more confidence and credibility in your work with machine learning.

The DP-100 exam is a great choice if you're looking to get deeper into machine learning on Azure, and you'll learn a lot while studying.

Microsoft Certified: Azure Data Scientist Associate is a great addition to your name, and it might even increase your odds of being accepted to speak at conferences.