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.
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