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2. Train Models & Run Experiments (25-30%):

  • Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
  • Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
  • Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
  • Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.

Passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is the major requirement for earning the Microsoft Certified: Azure Data Scientist Associate certification. This test measures the ability of the professionals to execute the following technical tasks: setting up an Azure Machine Learning workspace; running experiments & train models; optimizing and handling models; deploying and consuming models.

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DP-100 Exam Target Audience

Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Step 5: Studying with Books

Books is another efficient preparation tool. There are several guides available for the Microsoft DP-100 exam but the most popular are listed below:

  • Designing and Implementing a Data Science Solution on Azure - Exam Prep: Microsoft Azure DP-100 Certification Exam | Most Unique & Latest Questionnaires by VB Dev
  • Designing and Implementing a Data Science Solution on Azure: Prepare for exam DP-100 by Stavros Koureas
  • How I Passed DP-100: Designing and Implementing a Data Science Solution on Azure: Sure Shot Certification Tips by Empirical Matt Publications
  • Learn How to Pass DP-100: Designing and Implementing a Data Science Solution on Azure Exam: Learn at your fingertips by Rodrezil Publications
  • Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure 1st Edition by Pierstefano Tucci

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionWeightObjectives
Explore data and run experiments20-25%- Implement pipelines
  • 1. Build reusable components
  • 2. Pass data between steps
  • 3. Schedule and monitor pipelines
  • 4. Create and publish pipelines
- Explore and visualize data
  • 1. Detect anomalies and outliers
  • 2. Identify features and relationships
  • 3. Profile and validate data
- Run experiments
  • 1. Define parameters and configurations
  • 2. Track runs with MLflow
  • 3. Use automated machine learning
  • 4. Configure experiment runs
Optimize language models for AI applications25-30%- Evaluate and improve models
  • 1. Apply responsible generative AI
  • 2. Optimize for accuracy and safety
  • 3. Test and evaluate responses
- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Create vector stores and indexes
  • 3. Prepare and process data
- Implement generative AI solutions
  • 1. Apply prompt engineering
  • 2. Use Azure AI Foundry
  • 3. Build prompt flows
Train and deploy models25-30%- Deploy models
  • 1. Configure compute and scaling
  • 2. Secure endpoints and manage access
  • 3. Deploy to online endpoints
  • 4. Deploy to batch endpoints
- Monitor and maintain models
  • 1. Monitor performance and data drift
  • 2. Implement MLOps practices
  • 3. Update and retrain models
- Train models
  • 1. Apply responsible AI principles
  • 2. Configure jobs and environments
  • 3. Use HyperDrive for hyperparameter tuning
  • 4. Run training scripts
- Manage models
  • 1. Interpret models and explain predictions
  • 2. Register and version models
  • 3. Package and validate models
Design and prepare a machine learning solution20-25%- Design a machine learning solution
  • 1. Plan model deployment requirements
  • 2. Define compute specifications for workloads
  • 3. Select development approach
  • 4. Determine dataset structure and format
- Manage Azure Machine Learning workspace
  • 1. Set up Git integration
  • 2. Work with registries
  • 3. Create and configure workspace
  • 4. Use developer tools and CLI
- Manage data assets
  • 1. Create and maintain data assets
  • 2. Register and manage datastores
  • 3. Select storage services
- Manage compute resources
  • 1. Select environments
  • 2. Attach and monitor compute
  • 3. Create and configure compute targets

Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) Sample Questions:

150개 이상의 특징을 포함하는 데이터셋이 있습니다. 이 데이터셋을 사용하여 서포트 벡터 머신(SVM) 이진 분류기를 학습시킵니다.
데이터 세트에 대한 기능 중요도 점수를 계산하려면 Azure Machine Learning Studio의 순열 기능 중요도 모듈을 사용해야 합니다.
어떤 순서로 작업을 수행해야 할까요? 정답을 맞추려면 작업 목록에 있는 모든 작업을 정답 영역으로 옮기고 올바른 순서대로 배열하세요.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector- machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature- importance

Azure Machine Learning 작업 영역을 관리하고 있습니다. 서버리스 컴퓨팅으로 구성된 학습 작업을 설계했는데, 이 서버리스 컴퓨팅에는 특정 인스턴스 유형과 개수가 필요합니다. Azure Machine Learning Python SDK v2를 사용하여 서버리스 컴퓨팅을 구성해야 합니다. 어떻게 해야 할까요?

  • A. 티어 매개변수를 전용 VM으로 구성합니다.
  • B. ResourceConfiguration 클래스를 초기화하고 지정합니다.
  • C. 명령 작업의 compute 매개변수를 사용하여 컴퓨팅 이름을 지정합니다.
  • D. 크기와 유형 지정을 사용하여 AmICompute 클래스를 초기화합니다.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

로컬 워크스테이션에서 Azure Machine Learning Python SDK v2를 사용하여 모델을 등록하고 트램핑할 수 있습니다.
워크스테이션에 Python 3.7과 Visual Studio Code가 설치되어 있습니다.
Kubernetes 온라인 엔드포인트를 통해 프로덕션 환경에 모델을 배포하려고 하면 스코어링 스크립트에서 오류가 발생하여 배포가 실패합니다.
서비스를 운영 환경에 배포하기 전에 로컬 워크스테이션에서 서비스를 디버깅해야 합니다.
순서대로 수행해야 할 세 가지 행동은 무엇입니까? 정답을 선택하려면, 답변 영역에서 행동 목록에서 적절한 행동을 찾아 올바른 순서대로 배열하십시오.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

다중 클래스 이미지 분류 딥러닝 모델을 생성합니다.
PyTorch 버전 1.2를 사용하여 모델을 학습시킵니다.
모델을 배포할 때 추론 환경에서 올바른 버전의 PyTorch를 식별할 수 있도록 해야 합니다.
어떻게 해야 할까요?

  • A. .pt 파일 확장자와 기본 버전 속성을 사용하여 모델을 등록합니다.
  • B. 모델을 로컬에 .pt 파일로 저장하고, 로컬 웹 서비스로 배포합니다.
  • C. model_framework 및 model_framework_version 속성을 지정하여 모델을 등록합니다.
  • D. 기본 Azure Machine Learning conda 환경을 사용하도록 구성된 컴퓨터에 모델을 배포합니다.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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참고: 이 문제는 동일한 시나리오를 제시하는 일련의 문제 중 하나입니다. 각 문제에는 제시된 목표를 달성할 수 있는 고유한 해결책이 포함되어 있습니다. 일부 문제 세트에는 정답이 두 개 이상일 수 있으며, 정답이 없는 문제 세트도 있습니다.
이 섹션에서 질문에 답변한 후에는 해당 질문으로 돌아갈 수 없습니다. 따라서 이 질문들은 검토 화면에 나타나지 않습니다.
여러 열에 결측값이 포함된 수치 데이터 세트를 분석하고 있습니다.
결측값은 특징 집합의 차원에 영향을 주지 않는 적절한 연산을 사용하여 처리해야 합니다.
모든 값을 포함하려면 전체 데이터 세트를 분석해야 합니다.
해결 방법: 누락된 데이터가 포함된 열 전체를 제거합니다.
이 해결책은 목표를 달성합니까?

  • A. 아니요
  • B. 예
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

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