Microsoft Azure DP-100 Certification - Full exam preparation - Udemy Free Coupon


Microsoft Azure DP-100 Certification - Full exam preparation - Udemy Free Coupon

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340+ Exam Questions and Answers! One of the most detailed Azure Dp-100 exam preparation you will find on the web


Description


Looking for an perfect Azure DP-100 exam preparation? Searching since hours without good results? Congratulation!
You found what you need, without wasting time downloading a lot of stuff you need to sort anyway or which have wrong solutions!

You get an explanation whereever needed, so you don't need to begin research on your own.
The answers to the questions are validated.

Microsoft Certified: Azure Data Scientist Associate - Full exam preparation
Real exam details:
Duration: 180 Minutes, No. Of Questions: 40-60, Passing Score: 70%.

This course provides overall 340 unique questions for your Azure exam preparation!

We always want to deliver highest quality to you and we made our best to do so. If you find any issue let me know and we will correct it immediatelly :)

Well performing Azure practitioners have great long-term job oppertunities. Each higher level of Oracle certification brings a higher standard of benchmarking skill and ability, which leads to greater opportunities and higher pay.
What people say who are certified by Microsoft Azure:
23 percent received up to a 20 percent salary increase after obtaining certification
Nearly 65 percent of respondents received a positive impact on their professional image or reputation after obtaining certification
54 percent of those who obtained certifications experienced a career benefit within three months, and 24 percent experienced the benefit immediately.
Benefeits of getting further certified:
Added credibility
Good job opportunities
Azure Experts are in Demand
More than 80 percent of Fortune 500 companies are taking advantage of Microsoft Cloud
Data shows that around 90 percent of companies are taking some advantage of cloud technology
Exam Concepts:
Manage Azure resources for machine learning(25-30%)
Run experiments and train models (20-25%)
Deploy and operationalize machine learning solutions (35-40%)
Implement responsible machine learning (5-10%)

This course provide overall 540 unique questions for your exam preparation. There are no duplicated questions. All questions are multiple choice with one or several correct answer. You will get the information of how many answers are correct for each question as in the real exam.
There are 5 test exams for you. Each has 60 questions as the real exam has.
There sixth exam test contains 240 questions. You should do this test, after solving the first 5 tests with a very good score

You will learn about the following contents as they can be covered by the exam:
Exam Topics
Manage Azure resources for machine learning (25-30%)
Create an Azure Machine Learning workspace
create an Azure Machine Learning workspace
configure workspace settings
manage a workspace by using Azure Machine Learning studio
Manage data in an Azure Machine Learning workspace
select Azure storage resources
register and maintain datastores
create and manage datasets
Manage compute for experiments in Azure Machine Learning
determine the appropriate compute specifications for a training workload
create compute targets for experiments and training
configure Attached Compute resources including Azure Databricks
monitor compute utilization
Implement security and access control in Azure Machine Learning
determine access requirements and map requirements to built-in roles
create custom roles
manage role membership
manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment
create compute instances
share compute instances
access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace
create an Azure Databricks workspace
create an Azure Databricks cluster
create and run notebooks in Azure Databricks
link and Azure Databricks workspace to an Azure Machine Learning workspace
Run experiments and train models (20-25%)
Create models by using the Azure Machine Learning designer
create a training pipeline by using Azure Machine Learning designer
ingest data in a designer pipeline
use designer modules to define a pipeline data flow
use custom code modules in designer
Run model training scripts
create and run an experiment by using the Azure Machine Learning SDK
configure run settings for a script
consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
run a training script on Azure Databricks compute
run code to train a model in an Azure Databricks notebook
Generate metrics from an experiment run
log metrics from an experiment run
retrieve and view experiment outputs
use logs to troubleshoot experiment run errors
use MLflow to track experiments
track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models
use the Automated ML interface in Azure Machine Learning studio
use Automated ML from the Azure Machine Learning SDK
select pre-processing options
select the algorithms to be searched
define a primary metric
get data for an Automated ML run
retrieve the best model
Tune hyperparameters with Azure Machine Learning
select a sampling method
define the search space
define the primary metric
define early termination options
find the model that has optimal hyperparameter values
Deploy and operationalize machine learning solutions (35-40%)
Select compute for model deployment
consider security for deployed services
evaluate compute options for deployment
Deploy a model as a service
configure deployment settings
deploy a registered model
deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
consume a deployed service
troubleshoot deployment container issues
Manage models in Azure Machine Learning
register a trained model
monitor model usage
monitor data drift
Create an Azure Machine Learning pipeline for batch inferencing
configure a ParallelRunStep
configure compute for a batch inferencing pipeline
publish a batch inferencing pipeline
run a batch inferencing pipeline and obtain outputs
obtain outputs from a ParallelRunStep
Publish an Azure Machine Learning designer pipeline as a web service
create a target compute resource
configure an inference pipeline
consume a deployed endpoint
Implement pipelines by using the Azure Machine Learning SDK
create a pipeline
pass data between steps in a pipeline
run a pipeline
monitor pipeline runs
Apply ML Ops practices
trigger an Azure Machine Learning pipeline from Azure DevOps
automate model retraining based on new data additions or data changes
refactor notebooks into scripts
implement source control for scripts
Implement responsible machine learning (5-10%)
Use model explainers to interpret models
select a model interpreter
generate feature importance data
Describe fairness considerations for models
evaluate model fairness based on prediction disparity
mitigate model unfairness
Describe privacy considerations for data
describe principles of differential privacy
specify acceptable levels of noise in data and the effects on privacy
In case you have questions, do not hesitate to contact us.

Please be aware that we are working on this course on an ongoing basis. We always want to deliver highest quality to you and we try our best to do so. If you find any issue let us know and we will correct it immediatelly :)
Who this course is for: DP-100 Azure Exam preparation Certification Machine Learning Data Science AI Microsoft


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