100％合格DP-100過去問と真実的なDP-100試験概要

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and prepare a machine learning solution20-25%- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks
- Design an Azure Machine Learning workspace
  • 1. Configure security and access
  • 2. Manage compute resources
  • 3. Configure workspace resources
Topic 2: Deploy and retrain models10-15%- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows
- Monitor deployed models
  • 1. Track data drift
  • 2. Monitor model performance
Topic 3: Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy real-time inference endpoints
  • 2. Deploy batch inference pipelines
- Manage deployment assets
  • 1. Register models
  • 2. Create inference configurations
Topic 4: Explore data and train models35-40%- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
- Optimize model performance
  • 1. Evaluate models
  • 2. Improve accuracy and performance
- Run experiments and train models
  • 1. Use automated machine learning
  • 2. Track experiments
  • 3. Perform hyperparameter tuning

>> DP-100過去問 <<

DP-100試験概要、DP-100日本語

学歴は実力と等しくなく、能力とも等しくないです。本当の能力は実践で鍛えたもので、学歴と直接な関係がないです。「私はだめです。」と思わないでください。MicrosoftのDP-100試験に申し込んだあなたは自分が合格できないなんてを心配だったら、MogiExamのMicrosoftのDP-100試験トレーニング資料を利用してください。学歴がどんなに高くて、能力がどんなに低くても、首尾よく試験に合格することができます。

Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q464-Q469):

質問 # 464
You are performing a classification task in Azure Machine learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
Warning samples based on a provided data set.
You need to split the data with a 0.75:0.25.
Which value should you use for each parameter? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：


質問 # 465
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.

正解：B

解説：
Use the VM as a compute target.
Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target


質問 # 466
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

正解：

解説：

1 - from azureml . train.hyperdrive
2 - import TruncationSelectionPolicy
3 - early_termination_policy =....
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters


質問 # 467
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample.data folder into a Pandas data frame. You need to complete the Python SDK v2 code to use the MLTaWe folder as the materialization blueprint. How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 468
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 469
......

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