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Career Opportunities, Roles, and Salary

After obtaining the Microsoft Certified: Azure Data Scientist Associate certification, you become eligible for the data scientist role. Also, with this certification, you can expect to earn a decent remuneration of about $97k on average annually, as stated by PayScale.com. The expected career paths for a data scientist also include senior data scientist with a salary potential of $126,778 and data science manager with a salary potential of $136,781. Another possible path is that of a data science director with a potential pay of $157,095 annually. However, remember that besides, such a certificate work experience also matters.

Target Audience & Requirements

The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.

DP-100 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our DP-100 exam dumps will include the following topics:

  • Define and prepare the development environment 15-20%
  • Perform feature engineering 15-20%
  • Develop models 40-45%
  • Prepare data for modeling 25-30%

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

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Microsoft DP-100 Exam Syllabus Topics:

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

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