DP-100: Microsoft Azure Data Scientist Associate certification is a highly sought-after credential for professionals looking to demonstrate their expertise in data science and analytics using the Microsoft Azure platform. This certification is designed for individuals who have a strong background in data science and machine learning, and who are looking to advance their skills in working with data on the Azure cloud.

One of the key features of the DP-100 certification is the comprehensive practice exam that is included as part of the preparation process. This practice exam is designed to simulate the experience of taking the actual DP-100 exam, giving candidates the opportunity to familiarize themselves with the format and structure of the test. By taking the practice exam, candidates can identify areas where they may need to focus their study efforts, and gain confidence in their ability to successfully pass the certification exam.

DP-100 certification exam covers a wide range of topics related to data science and machine learning on the Azure platform. Candidates will be tested on their ability to design and implement data models, build and deploy machine learning models, and work with data in a variety of formats. The exam also covers topics such as data visualization, feature engineering, and model evaluation, ensuring that candidates have a comprehensive understanding of the key concepts and techniques used in data science.

In order to prepare for the DP-100 exam, candidates are encouraged to take advantage of the resources available through Microsoft's official training program. This program includes a series of online courses and tutorials that cover the key topics and skills needed to pass the exam. Candidates can also access practice exams and other study materials to help them prepare for the certification exam.

Once candidates have successfully passed the DP-100 exam, they will be awarded the Microsoft Azure Data Scientist Associate certification. This credential is recognized by employers and industry professionals as a mark of expertise in data science and analytics on the Azure platform. With the DP-100 certification, professionals can demonstrate their ability to work with data effectively, build and deploy machine learning models, and drive insights and value for their organizations.

DP-100: Microsoft Azure Data Scientist Associate certification is a valuable credential for professionals looking to advance their careers in data science and analytics. With a comprehensive practice exam, a wide range of topics covered, and access to Microsoft's official training program, candidates can prepare effectively for the certification exam and demonstrate their expertise in working with data on the Azure platform. By earning the DP-100 certification, professionals can enhance their skills, advance their careers, and stand out in the competitive field of data science.

DP-100: Microsoft Azure Data Scientist Associate Exam Summary:

• Exam Name : Microsoft Certified - Azure Data Scientist Associate

• Exam code: DP-100

• Exam voucher cost: $165 USD

• Exam languages: English, Japanese, Korean, and Simplified Chinese

• Exam format: Multiple-choice, multiple-answer

• Number of questions: 40-60 (estimate)

• Length of exam: 120minutes

• Passing grade: Score is from 700-1000.

DP-100: Microsoft Azure Data Scientist Associate Exam Syllabus Topics:

• Design and prepare a machine learning solution (20–25%)

• Explore data and train models (35–40%)

• Prepare a model for deployment (20–25%)

• Deploy and retrain a model (10–15%)

Design and prepare a machine learning solution (20–25%)

Design a machine learning solution

• Determine the appropriate compute specifications for a training workload

• Describe model deployment requirements

• Select which development approach to use to build or train a model

Manage an Azure Machine Learning workspace

• Create an Azure Machine Learning workspace

• Manage a workspace by using developer tools for workspace interaction

• Set up Git integration for source control

• Create and manage registries

Manage data in an Azure Machine Learning workspace

• Select Azure Storage resources

• Register and maintain datastores

• Create and manage data assets

Manage compute for experiments in Azure Machine Learning

• Create compute targets for experiments and training

• Select an environment for a machine learning use case

• Configure attached compute resources, including Apache Spark pools

• Monitor compute utilization

Explore data and train models (35–40%)

Explore data by using data assets and data stores

• Access and wrangle data during interactive development

• Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer

• Create a training pipeline

• Consume data assets from the designer

• Use custom code components in designer

• Evaluate the model, including responsible AI guidelines

Use automated machine learning to explore optimal models

• Use automated machine learning for tabular data

• Use automated machine learning for computer vision

• Use automated machine learning for natural language processing

• Select and understand training options, including preprocessing and algorithms

• Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

• Develop code by using a compute instance

• Track model training by using MLflow

• Evaluate a model

• Train a model by using Python SDKv2

• Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning

• Select a sampling method

• Define the search space

• Define the primary metric

• Define early termination options

Prepare a model for deployment (20–25%)

Run model training scripts

• Configure job run settings for a script

• Configure compute for a job run

• Consume data from a data asset in a job

• Run a script as a job by using Azure Machine Learning

• Use MLflow to log metrics from a job run

• Use logs to troubleshoot job run errors

• Configure an environment for a job run

• Define parameters for a job

Implement training pipelines

• Create a pipeline

• Pass data between steps in a pipeline

• Run and schedule a pipeline

• Monitor pipeline runs

• Create custom components

• Use component-based pipelines

Manage models in Azure Machine Learning

• Describe MLflow model output

• Identify an appropriate framework to package a model

• Assess a model by using responsible AI guidelines

Deploy and retrain a model (10–15%)

Deploy a model

• Configure settings for online deployment

• Configure compute for a batch deployment

• Deploy a model to an online endpoint

• Deploy a model to a batch endpoint

• Test an online deployed service

• Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices

• Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

• Automate model retraining based on new data additions or data changes

• Define event-based retraining triggers

  
  

In conclusion, the DP-100: Microsoft Azure Data Scientist Associate certification is a valuable credential for professionals looking to advance their careers in data science and analytics. With a comprehensive practice exam, a wide range of topics covered, and access to Microsoft's official training program, candidates can prepare effectively for the certification exam and demonstrate their expertise in working with data on the Azure platform. By earning the DP-100 certification, professionals can enhance their skills, advance their careers, and stand out in the competitive field of data science.