DP-100 Designing and Implementing Data Science Solution 2023

DP-100 Designing and Implementing Data Science Solution 2023, available at $19.99, 4 quizzes.

You will learn about Understand various Data Science concepts for Azure Pass the DP-100 Exam Comprehending diverse Azure assets for addressing distinct Data Analytics challenges. Implement various Data Science solutions on Azure platform This course is ideal for individuals who are IT professionals aspiring to become Cloud Data Scientists or Data Science students preparing for the DP-100 certification exam or Individuals seeking to validate their Data Science skills and knowledge It is particularly useful for IT professionals aspiring to become Cloud Data Scientists or Data Science students preparing for the DP-100 certification exam or Individuals seeking to validate their Data Science skills and knowledge.

Enroll now: DP-100 Designing and Implementing Data Science Solution 2023

Summary

Title: DP-100 Designing and Implementing Data Science Solution 2023

Price: $19.99

Number of Quizzes: 4

Number of Published Quizzes: 4

Number of Curriculum Items: 4

Number of Published Curriculum Objects: 4

Number of Practice Tests: 4

Number of Published Practice Tests: 4

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Understand various Data Science concepts for Azure

Pass the DP-100 Exam

Comprehending diverse Azure assets for addressing distinct Data Analytics challenges.

Implement various Data Science solutions on Azure platform

Who Should Attend

IT professionals aspiring to become Cloud Data Scientists

Data Science students preparing for the DP-100 certification exam

Individuals seeking to validate their Data Science skills and knowledge

Target Audiences

IT professionals aspiring to become Cloud Data Scientists

Data Science students preparing for the DP-100 certification exam

Individuals seeking to validate their Data Science skills and knowledge

Are you aiming to achieve the prestigious “Microsoft Certified: Azure Data Scientist Associate” certification? Look no further! Our comprehensive practice test is specially curated to test your knowledge and prepare you with 100% confidence to ace the DP-100 examination.

Designed with utmost care, the questions in this practice test are either directly sourced from Azure Documentation or represent real-world data engineering scenarios. This ensures that you are well-prepared for the challenges you may encounter during the exam.

Each question is accompanied by detailed explanations and links to the corresponding Microsoft documentation, where the concept or scenario was framed. By taking this practice test, you will not only gain a deep understanding of the subject matter but also get accustomed to the format of the actual DP-100 exam.

Our practice test is regularly updated to reflect any changes in the testing areas by Microsoft. You can rest assured that you are practicing with the most relevant and up-to-date content, giving you a competitive edge in the certification process.

The objectives covered in this course are:

Design and prepare a machine learning solution (20–25%)

Design a machine learning solution

Determine the appropriate compute specifications for a training workload

Describe model deployment requirements

Select which development approach to use to build or train a model

Manage an Azure Machine Learning workspace

Create an Azure Machine Learning workspace

Manage a workspace by using developer tools for workspace interaction

Set up Git integration for source control

Manage data in an Azure Machine Learning workspace

Select Azure Storage resources

Register and maintain datastores

Create and manage data assets

Manage compute for experiments in Azure Machine Learning

Create compute targets for experiments and training

Select an environment for a machine learning use case

Configure attached compute resources, including Apache Spark pools

Monitor compute utilization

Explore data and train models (35–40%)

Explore data by using data assets and data stores

Access and wrangle data during interactive development

Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer

Create a training pipeline

Consume data assets from the designer

Use custom code components in designer

Evaluate the model, including responsible AI guidelines

Use automated machine learning to explore optimal models

Use automated machine learning for tabular data

Use automated machine learning for computer vision

Use automated machine learning for natural language processing (NLP)

Select and understand training options, including preprocessing and algorithms

Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

Develop code by using a compute instance

Track model training by using MLflow

Evaluate a model

Train a model by using Python SDKv2

Use the terminal to configure a compute instance

Tune hyperparameters with Azure Machine Learning

Select a sampling method

Define the search space

Define the primary metric

Define early termination options

Prepare a model for deployment (20–25%)

Run model training scripts

Configure job run settings for a script

Configure compute for a job run

Consume data from a data asset in a job

Run a script as a job by using Azure Machine Learning

Use MLflow to log metrics from a job run

Use logs to troubleshoot job run errors

Configure an environment for a job run

Define parameters for a job

Implement training pipelines

Create a pipeline

Pass data between steps in a pipeline

Run and schedule a pipeline

Monitor pipeline runs

Create custom components

Use component-based pipelines

Manage models in Azure Machine Learning

Describe MLflow model output

Identify an appropriate framework to package a model

Assess a model by using responsible AI guidelines

Deploy and retrain a model (10–15%)

Deploy a model

Configure settings for online deployment

Configure compute for a batch deployment

Deploy a model to an online endpoint

Deploy a model to a batch endpoint

Test an online deployed service

Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices

Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

Automate model retraining based on new data additions or data changes

Define event-based retraining triggers

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building Machine Learning solutions, alongside with the knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

“All the best for your exam”

Course Curriculum

Instructors

Sanjay Palod
Cloud Developer

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