DP-100 Practice Exam Actual Practice Questions

DP-100 Practice Exam – Actual & Practice Questions, available at $19.99, has an average rating of 3.71, 3 quizzes, based on 7 reviews, and has 946 subscribers.

You will learn about Able to get better concepts on Azure Machine learning This course is ideal for individuals who are Anyone who want to write DP-100 Certification exam It is particularly useful for Anyone who want to write DP-100 Certification exam.

Enroll now: DP-100 Practice Exam – Actual & Practice Questions

Summary

Title: DP-100 Practice Exam – Actual & Practice Questions

Price: $19.99

Average Rating: 3.71

Number of Quizzes: 3

Number of Published Quizzes: 3

Number of Curriculum Items: 3

Number of Published Curriculum Objects: 3

Number of Practice Tests: 3

Number of Published Practice Tests: 3

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Able to get better concepts on Azure Machine learning

Who Should Attend

Anyone who want to write DP-100 Certification exam

Target Audiences

Anyone who want to write DP-100 Certification exam

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

Latest Update onMarch 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

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

Course Curriculum

Instructors

Dimpal Solanki
Data Scientist

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2 stars: 2 votes

3 stars: 1 votes

4 stars: 2 votes

5 stars: 2 votes

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