DP-100 Practice Exam – Actual & Practice Questions

DP-100 Practice Exam – Actual & Practice Questions, Anyone who wanted to clear the DP-100 Exam in first try.

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

Skills measured

• Set up an Azure Machine Learning workspace (30-35%)
• Run experiments and train models (25-30%)
• Optimize and manage models (20-25%)
• Deploy and consume models (20-25%)

Detail Skills

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

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