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Microsoft Azure Administrator Certification Transition

Designing and Implementing a Data Science Solution on Azure (beta)

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    Fulfills requirements for Microsoft Certified: Azure Data Scientist Associate

    With Microsoft Certification, technology professionals are more likely to get hired, demonstrate clear business impact, and advance their careers.

    About the certification
* 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.

Skills measured

This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

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Define and prepare the development environment (15-20%)
  • Select development environment
    • May include but is not limited to: Assess the deployment environment constraints, analyze and recommend tools that meet system requirements, select the development environment
  • Set up development environment
    • May include but is not limited to: Create an Azure data science environment, configure data science work environments
  • Quantify the business problem
    • May include but is not limited to: Define technical success metrics, quantify risks
Prepare data for modeling (25-30%)
  • Transform data into usable datasets
    • May include but is not limited to: Develop data structures, design a data sampling strategy, design the data preparation flow
  • Perform Exploratory Data Analysis (EDA)
    • May include but is not limited to: 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
    • May include but is not limited to: Resolve anomalies, outliers, and other data inconsistencies, standardize data formats, set the granularity for data
Perform Feature Engineering (15-20%)
  • Perform feature extraction
    • May include but is not limited to: Perform feature extraction algorithms on numerical data, perform feature extraction algorithms on non-numerical data, scale features
  • Perform feature selection
    • May include but is not limited to: Define the optimality criteria, apply feature selection algorithms
Develop models (40-45%)
  • Select an algorithmic approach
    • May include but is not limited to: Determine appropriate performance metrics, implement appropriate algorithms, consider data preparation steps that are specific to the selected algorithms
  • Split datasets
    • May include but is not limited to: 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
    • May include but is not limited to: Resample a dataset to impose balance, adjust performance metric to resolve imbalances, implement penalization
  • Train the model
    • May include but is not limited to: Select early stopping criteria, tune hyper-parameters
  • Evaluate model performance
    • May include but is not limited to: Score models against evaluation metrics, implement cross-validation, identify and address overfitting, identify root cause of performance results

Preparation options

  • Learning content will be available on March 15, 2019.

Who should take this exam?

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.

More information about exams

Preparing for an exam

We recommend that you review this exam preparation guide in its entirety and familiarize yourself with the resources on this website before you schedule your exam. See the Microsoft Certification exam overview for information about registration, videos of typical exam question formats, and other preparation resources. For information on exam policies and scoring, see the Microsoft Certification exam policies and FAQs.


This preparation guide is subject to change at any time without prior notice and at the sole discretion of Microsoft. Microsoft exams might include adaptive testing technology and simulation items. Microsoft does not identify the format in which exams are presented. Please use this preparation guide to prepare for the exam, regardless of its format. To help you prepare for this exam, Microsoft recommends that you have hands-on experience with the product and that you use the specified training resources. These training resources do not necessarily cover all topics listed in the "Skills measured" section.