تدرب على الامتحانات | MS Azure DP-100 Design & Implement DS Sol

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In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

يحتوي كل سؤال على شرح مفصل وروابط لمواد مرجعية لدعم الإجابات التي تضمن دقة حلول المشكلة.

سيتم خلط الأسئلة عشوائيًا في كل مرة تكرر فيها الاختبارات ، لذا ستحتاج إلى معرفة سبب صحة الإجابة, ليس فقط أن الإجابة الصحيحة كانت العنصر “ب” آخر مرة خضت فيها الاختبار.

The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; خاصه, using Azure Machine Learning Service and Azure Databricks. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. بالاضافة, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Skills measured on Microsoft Azure DP-100 Exam

Set up an Azure Machine Learning Workspace (30-35%)

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 objects in an Azure Machine Learning workspace

register and maintain datastores
create and manage datasets

Manage experiment compute contexts

create a compute instance
determine appropriate compute specifications for a training workload
create compute targets for experiments and training

Run Experiments and Train Models (25-30%)

Create models by using 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 training scripts in an Azure Machine Learning workspace

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

Generate metrics from an experiment run

log metrics from an experiment run
retrieve and view experiment outputs
use logs to troubleshoot experiment run errors

Automate the model training process

create a pipeline by using the SDK
pass data between steps in a pipeline
run a pipeline
monitor pipeline runs

Optimize and Manage Models (20-25%)

Use Automated ML 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
determine algorithms to be searched
define a primary metric
get data for an Automated ML run
retrieve the best model

Use Hyperdrive to tune hyperparameters

select a sampling method
define the search space
define the primary metric
define early termination options
find the model that has optimal hyperparameter values

Use model explainers to interpret models

select a model interpreter
generate feature importance data

Manage models

register a trained model
monitor model usage
monitor data drift

Deploy and Consume Models (20-25%)

Create production compute targets

consider security for deployed services
evaluate compute options for deployment

Deploy a model as a service

configure deployment settings
consume a deployed service
troubleshoot deployment container issues

Create a pipeline for batch inferencing

publish a batch inferencing pipeline
run a batch inferencing pipeline and obtain outputs

Publish a designer pipeline as a web service

create a target compute resource
configure an Inference pipeline
consume a deployed endpoint

الاختبار متاح باللغات التالية: الإنجليزية, اليابانية, صينى (مبسط), الكورية