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The benefit in Obtaining the DP-100 Exam Certification

  • Becoming Microsoft Certified Azure Data Scientist Associate means one thing you are worth more to the company and therefore more to yourself in the form of an upgraded pay package. On average a Microsoft Certified Azure Data Scientist Associate member of staff is estimated to be worth 30% more to a company than their uncertified professionals.
  • Organization owners invest a lot in their employees when it comes to their training with the goal of making them quicker, more efficient, and more knowledgeable about their role. Certified Professional will reduce the time he spends on tasks, meaning he can get more done this could help reduce company downtime when repairing faults on a system or fixing hardware problems.
  • Candidates will get in-depth knowledge by completing the courses along with the access to revision materials for 6 months upon completion means they will have a wider skill set when it comes to the various technologies and systems than an uncertified professional. Certified Professional in this particular skill set is 74% more efficient when it comes to completing their tasks in a timely well-executed manner.
  • After completion of Microsoft Certified Azure Data Scientist Associate Certification candidates receive official confirmation from Microsoft that you are now fully certified in their chosen field. This can be now added to their CV, cover letters and job applications.
  • When Candidates applying for a job or looking to promotion in their current position, a Microsoft Certified Azure Data Scientist Associate certification in the field in which Candidates are applying will put you at the top of the list and make them a desirable candidate for employers.

Knowing Associated Certification

The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 exam will also help the test-taker to gain some ACE college credits.

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

Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:

Duration120 mins
Sample QuestionsMicrosoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Books / TrainingDP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Passing Score700 / 1000
Schedule ExamPearson VUE
Exam NameMicrosoft Certified - Azure Data Scientist Associate
Exam Price$165 (USD)
Exam CodeDP-100
Number of Questions40-60

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

  • 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.
  • Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
  • 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.
  • 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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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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