DP-100 Practice Exam – Actual & Practice Questions

DP-100 Practice Exam – Actual & Practice Questions

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DP-100 Practice Exam – Actual & Practice Questions

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

Data Scientist is most demanded skill of this era.
Certified Data Scientist get more chance to get hired than non-certified candidate.


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