DP-100 Practice Exam - Actual & Practice Questions

Latest Update - March 2024. Anyone who wanted to clear the DP-100 Exam in first try

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DP-100 Practice Exam - Actual & Practice Questions
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119 questions
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Mar 2024
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$19.99
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What you will learn

Able to get better concepts on Azure Machine learning

Description

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


Latest Update on March 2024. Exam questions that have been carefully selected to ensure your success on the first try. 150+ different questions. More questions will be added soon...


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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Reviews

Jason
January 10, 2023
I find that there could have been more video or pop-up explanations...and more practice tests than just two. But other than that, well done!

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udemy ID
11/26/2020
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12/5/2020
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