DP-100 (Microsoft Azure Data Science Solution) Exam Study Guide

Preparing for DP-100 Designing and Implementing a Data Science Solution on Azure Certificate exam? Don’t know where to start? This post is the DP-100 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of DP-100 exam. I hope this article will help you to prepare for DP-100 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.

DP-100 Course (Online Training)

Pluralsight (Learning Path)	Microsoft Azure Data Scientist Course
LinkedIn Learning (Free trial)	Azure Machine Learning Development
Udemy	A-Z Machine Learning using Azure Machine Learning

DP-100 Practice Tests & Labs

Udemy Practice Test	70+ Practice Exam Questions
DP-100 Labs on GitHub	Implementing an Azure Data Science Solution

DP-100 E-book (PDF)

Amazon

Azure Data Scientist 48 Test Prep Questions

DP-100 Exam Voucher

Test Voucher

Microsoft Azure Single Shot Exam Voucher ($30 OFF)

To view other Azure Certificate Study Guides, click here

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

Select development environment

Assess the deployment environment constraints

https://docs.microsoft.com/en-us/learn/modules/register-and-deploy-model-with-amls/8-where-to-deploy-model

Analyze and recommend tools that meet system requirements

https://docs.microsoft.com/en-us/learn/modules/choose-data-science-option-in-azure/index

Select the development environment

https://docs.microsoft.com/en-us/learn/modules/choose-data-science-option-in-azure/index

Set up development environment

Create an Azure data science environment

https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/environment-setup

https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace

Configure data science work environments

https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment

Quantify the business problem

Define technical success metrics

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

Quantify risks

https://www.oliverwyman.com/our-expertise/insights/2018/dec/risk-journal-vol8-rethinking-tactics/the-risk-of-machine-learning-bias-and-how-to-prevent-it.html

https://www.oreilly.com/radar/managing-risk-in-machine-learning/

Prepare data for modeling (25-30%)

Transform data into usable datasets

Develop data structures

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/data-format-conversions

Design a data sampling strategy

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

Design the data preparation flow

https://azure.microsoft.com/en-us/resources/videos/preprocessing-data-in-azure-ml-studio/

Perform Exploratory Data Analysis (EDA)

Review visual analytics data to discover patterns and determine next steps

https://docs.microsoft.com/en-us/learn/create-data-visualizations-using-azure-databricks-and-power-bi/3-complete-labs-in-azure-databricks

https://docs.microsoft.com/en-us/learn/modules/analyze-climate-data-with-azure-notebooks/2-upload-data-and-create-scatterplot

https://docs.microsoft.com/en-us/learn/modules/analyze-climate-data-with-azure-notebooks/5-analyze-data-with-seaborn

Identify anomalies, outliers, and other data inconsistencies

https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/prepare-data

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

Create descriptive statistics for a dataset

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data

Cleanse and transform data

Resolve anomalies, outliers, and other data inconsistencies

The module ‘Preparing Input Data for Machine Learning Models’ covers most of the objectives

Standardize data formats

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-csv

Set the granularity for data

View this Reddit discussion on data granularity in Machine Learning

Video on choosing Data Granularity

Amazon link (affiliate)

Perform feature engineering (15-20%)

Perform feature extraction

Perform feature extraction algorithms on numerical data

https://docs.microsoft.com/en-us/learn/modules/intro-to-data-science-in-azure/6-feature-engineering

https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

Perform feature extraction algorithms on non-numerical data

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/data-transformation-learning-with-counts

Scale features

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/normalize-data

Perform feature selection

Define the optimality criteria

https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-parameters-optimize

Apply feature selection algorithms

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

Amazon link (affiliate)

Develop models (40-45%)

Select an algorithmic approach

Determine appropriate performance metrics

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/evaluate-model#metrics

Implement appropriate algorithms

https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice

Consider data preparation steps that are specific to the selected algorithms

Check the module ‘Identify Data-level Issues In Machine Learning Models’

Split datasets

Determine ideal split based on the nature of the data

https://www.freecodecamp.org/news/what-to-do-when-your-training-and-testing-data-come-from-different-distributions-d89674c6ecd8/

Determine number of splits

Coursera video on Data splitting strategies

Determine relative size of splits

https://stackoverflow.com/questions/is-there-a-rule-of-thumb-for-how-to-divide-a-dataset-into-training-and-validation

Ensure splits are balanced

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data-using-split-rows#divide-a-dataset-into-two-groups (Understand the concept Stratified split)

Identify data imbalances

Resample a dataset to impose balance

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

Adjust performance metric to resolve imbalances

https://blogs.msdn.microsoft.com/using-roc-plots-and-the-auc-measure-in-azure-ml/

Implement penalization

https://docs.microsoft.com/en-us/archive/msdn-magazine/2015/february/test-run-l1-and-l2-regularization-for-machine-learning

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression#how-to-configure-linear-regression

Train the model

Select early stopping criteria

https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters#specify-early-termination-policy

Tune hyper-parameters

https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters

Check the FAQs on Azure Certification

Evaluate model performance

Score models against evaluation metrics

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-score

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-model

Implement cross-validation

Demo: Cross-validation in Azure Machine Learning

Identify and address overfitting

PS Course Module: Recognize causes of overfitting

Identifying and addressing overfitting

Identify root cause of performance results

Check the first module ‘Feedback loops and Monitoring’

This brings us to the end of DP-100 Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

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