Azure DP 100 Exam Practice Tests

Covered all topics from official Microsoft DP 100 exam guidelines.

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