DP-100 Designing and Implementing a Data Science Solution on Azure (beta)

Languages: English
Audiences: IT Professionals
Technology: Microsoft Azure

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.