Microsoft DP-100日本語 exam - in .pdf

DP-100日本語 pdf
  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 07, 2026
  • Q & A: 528 Questions and Answers
  • PDF Price: $69.99
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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 07, 2026
  • Q & A: 528 Questions and Answers
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  • Exam Code: DP-100日本語
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)
  • Updated: Sep 07, 2026
  • Q & A: 528 Questions and Answers
  • Software Price: $69.99
  • Testing Engine

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

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. In addition, 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.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

Download exam skills outline

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DP-100 Exam Outline

The Microsoft DP-100 was recently renewed to meet the most current market needs and now it measures the following skills:

  • Deploying and Consuming Models;
  • Setting Up the Workspace for Azure Machine Learning;
  • Optimizing and Managing Models;
  • Running Experiments and Training Models.

The DP-100 exam domain of Setting Up the Workspace for Azure Machine Learning (ML) has three sections. The first touches on creating the workspace for ML. Here, you're to come across tasks like creating and configuring the workspace and managing it using Azure ML studio. The next part is concerning data object management within the workspace of Azure ML, where the focus goes to registering and maintaining datasets. The final aspect regards maintaining contexts for experiment compute. Under this, there will be creating instances for compute, determining the appropriate specs for compute targeting workload training, and developing targets for compute directed at experiments as well as training.

Regarding Optimizing and Managing Models, candidates will build their skills in five crucial areas. To begin is the area of creating optimal models using automated ML. This takes into account areas like Azure ML studio, Azure ML SDK, scaling options for pre-processing, algorithm determination, and getting data to be utilized in running the automated ML. The next thing goes into tuning hyperparameters using hyperdrive. Candidates need to note the sampling methods, search space, primary metric, termination options, and the right model. Another field concerns managing models where coverage includes model interpreters and feature importance data. Finally, students will learn how to manage models by exploring trained model registration, monitoring model usage, and monitoring data drift.

The Microsoft DP-100 exam also deals with the Deploying and Consuming Models. Of interest, there are four sections. It starts with the creation of targets for production compute involving security meant for deployed services & compute options targeting deployment. It's followed by the part of deploying a model as a service. This touches deployment settings, consuming deployed services, and troubleshooting issues for deployment containers. The next segment is creating a batch interference pipeline. Finally, students look at publishing a web service in the form of a designer pipeline. Issues also covered are compute resource, inference pipeline, and consumption of an already deployed endpoint.

The last DP-100 exam domain talks about Running Experiments and Training Models. The first way to achieve abilities in this area is by learning how to use Azure ML Designer to create models. This will be actualized by exploring creation of a training pipeline, ingestion of data within a designer pipeline, defining data flow for a pipeline using designer modules, and using modules for custom code. The second one regards running training scripts within the Azure ML workspace. Within this sphere, the students' focus will be how to use the Azure ML SDK in consuming data from a dataset in an experiment. The third thing in this topic has to do with using an experiment run to generate metrics. Here, learning includes log metrics, retrieving and viewing experiment outputs, and troubleshooting experiment errors using logs. The fourth and final area of concern is automating the process of model training. This includes developing a pipeline by utilizing the SDK, passing data, running a pipeline, and monitoring pipeline runs.

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

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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

  • Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
  • Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.
  • Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.

4. Models Deployment and Consumption (20-25%):

  • Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
  • Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
  • Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
  • Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.

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Microsoft DP-100日本語 Exam Syllabus Topics:

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

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