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Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Candidates for this exam apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. Candidates use machine learning techniques to train, evaluate,... and deploy models to build AI solutions that satisfy business objectives. Candidates use applications that involve natural language processing, speech, computer vision, and predictive analytics.

Candidates serve as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution. Candidates typically have background in mathematics, statistics, and computer science.

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Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Languages: English

This exam measures your ability to accomplish the following technical tasks: define and prepare the development environment; prepare data for modeling; perform feature engineering, develop models.

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Skills measured

Define and prepare the development environment (15-20%)

Select development environment

  • assess the deployment environment constraints
  • analyze and recommend tools that meet system requirements
  • select the development environment

Set up development environment

  • create an Azure data science environment
  • configure data science work environments

Quantify the business problem

  • define technical success metrics
  • quantify risks

Prepare data for modeling (25-30%)

Transform data into usable datasets

  • develop data structures
  • design a data sampling strategy
  • design the data preparation flow

Perform Exploratory Data Analysis (EDA)

  • review visual analytics data to discover patterns and determine next steps
  • identify anomalies, outliers, and other data inconsistencies
  • create descriptive statistics for a dataset

Cleanse and transform data

  • resolve anomalies, outliers, and other data inconsistencies
  • standardize data formats
  • set the granularity for data

Perform feature engineering (15-20%)

Perform feature extraction

  • perform feature extraction algorithms on numerical data
  • perform feature extraction algorithms on non-numerical data
  • scale features

Perform feature selection

  • define the optimality criteria
  • apply feature selection algorithms

Develop models (40-45%)

Select an algorithmic approach

  • determine appropriate performance metrics
  • implement appropriate algorithms
  • consider data preparation steps that are specific to the selected algorithms

Split datasets

  • determine ideal split based on the nature of the data
  • determine number of splits
  • determine relative size of splits
  • ensure splits are balanced

Identify data imbalances

  • resample a dataset to impose balance
  • adjust performance metric to resolve imbalances
  • implement penalization

Train the model

  • select early stopping criteria
  • tune hyper-parameters

Evaluate model performance

  • score models against evaluation metrics
  • implement cross-validation
  • identify and address overfitting
  • identify root cause of performance results

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Related certifications

Microsoft Certified: Azure Data Scientist Associate

Azure Data Scientists apply Azure’s machine learning techniques to train, evaluate, and deploy models that solve business problems.

* Pricing does not reflect any promotional offers or reduced pricing for Microsoft Imagine Academy program members, Microsoft Certified Trainers, and Microsoft Partner Network program members. Pricing is subject to change without notice. Pricing does not include applicable taxes. Please confirm exact pricing with the exam provider before registering to take an exam.

Additional resources

Guides to Role-based Certifications

Explore all certifications in a concise certification guide or the Role-based Certification Roadmap poster.

Training guide

Discover training resources to become a Microsoft Certified Azure Data Scientist Associate.