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3. Models Management and Optimization (20-25%):

  • Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
  • Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.

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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 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.
  • 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.

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

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Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Use SDKs and notebooks
  • 2. Configure environments
- Design an Azure Machine Learning workspace
  • 1. Manage compute resources
  • 2. Configure workspace resources
  • 3. Configure security and access
Topic 2: Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
Topic 3: Deploy and retrain models10-15%- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows
- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
Topic 4: Explore data and train models35-40%- Prepare data for modeling
  • 1. Ingest and transform data
  • 2. Manage datasets and datastores
- Run experiments and train models
  • 1. Use automated machine learning
  • 2. Perform hyperparameter tuning
  • 3. Track experiments
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models
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