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Skills measured

  • Deploy and operationalize machine learning solutions (35–40%)
  • Manage Azure resources for machine learning (25–30%)
  • Run experiments and train models (20–25%)
  • Implement responsible machine learning (5–10%)

Believe it or not, we face the more intense society, and we should prompt our competitiveness and get a Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) certification to make our dreams come true. Although it is not an easy thing to achieve it, once you choose our Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) prepare torrent, we will send the new updates for one year long, which is new enough to deal with the exam for you and guide you through difficulties in your exam preparation.

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Ways to Getting DP-100 Exam-Ready

The kind of extensive subject matter for the Microsoft DP-100 can be only obtained once the aspirant has access to quality study resources that are in general endorsed by the vendor itself. Overall, Microsoft offers both free and paid training courses to gain demanded expertise for DP-100. When it comes to the gratis learning path, it contains the following self-paced classes:

  • Build AI Solutions with Machine Learning - This is the mid-tier class offered for those who want to get themselves familiarized with Azure ML Python SDK that is used for creating organizational-ready AI solutions. Consisting of 14 full-length parts, the training requires you to possess some experience dealing with Python, PyTorch, ML, Scikit-Learn, and Tensorflow.
  • Create Machine Learning Model - This is an intermediate-level free training course with 5 modules that details every single fact you should know about ML and its functions, features, and tools. To enroll for this option, it’s advisable that you have some related experience with the Python programming language.
  • Create No-Code Predictive Models with Azure Machine Learning - In all, it’s beginner training that is composed of 4 concise modules that accentuate such technologies as data science, ML, Azure, and AI engineering. To be entitled to access this learning path and entirely apprehend all the details covered here, you are expected to have some skills in navigating the Azure portal.

As far as you can see, all these are the best options to consider when free learning resources for the Microsoft DP-100 exam are concerned. Still, there is one paid instructor-led training course as well. It covers the concepts related to Designing & Implementing a Data Science Solution on Azure and is known as Course DP-100T01-A, which is a 3-day long class explaining every bit of the exam domains. In particular, the course imparts in applicants the intermediate knowledge of ML solutions, Python to monitor data preparation & ingestion, and ML frameworks. During the whole training, you’ll get exposed to as many as 10 comprehensive modules that spin around the test objectives. What makes these lessons significant is that they contain labs in the curriculum unlike free self-paced learning, which allows you to develop practical skills that are generally necessary for job-oriented tasks. So, visit the vendor’s website, check whether you comply with the course prerequisites and if you do, enroll in it, and be entirely geared up for the Microsoft DP-100 exam.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionObjectives
Topic 1: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints
Topic 2: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle
Topic 3: Train machine learning models- Tune hyperparameters and evaluate models
- Train models using Azure Machine Learning
Topic 4: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 5: Design and prepare a machine learning solution- Select appropriate Azure services for machine learning workloads
- Manage compute and data assets
- Plan and configure Azure Machine Learning workspace

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