최신버전DP-100최고품질덤프공부자료덤프자료는Designing and Implementing a Data Science Solution on Azure시험패스의가장좋은자료

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Microsoft DP-100 Exam Syllabus Topics:

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

>> DP-100최고품질 덤프공부자료 <<

DP-100인기시험, DP-100최신버전 덤프샘플 다운

ExamPassdump의 Microsoft DP-100덤프는 IT업계에 오랜 시간동안 종사한 전문가들의 끊임없는 노력과 지금까지의 노하우로 만들어낸Microsoft DP-100시험대비 알맞춤 자료입니다. ExamPassdump의 Microsoft DP-100덤프만 공부하시면 여러분은 충분히 안전하게 Microsoft DP-100시험을 패스하실 수 있습니다. ExamPassdump Microsoft DP-100덤프의 도움으로 여러분은 IT업계에서 또 한층 업그레이드 될것입니다

최신 Microsoft Azure DP-100 무료샘플문제 (Q357-Q362):

질문 # 357
You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio.
The dataset contains categorical features that are highly correlated to the output label column.
You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use?

정답：B

설명：
Explanation
Pearson's correlation statistic, or Pearson's correlation coefficient, is also known in statistical models as the r value. For any two variables, it returns a value that indicates the strength of the correlation Pearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://www.statisticssolutions.com/pearsons-correlation-coefficient/


질문 # 358
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
Batch size must be 16, 32 and 64.
Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters


질문 # 359
You manage an Azure Machine Learning workspace.
You train a model interactively with a Jupyter Notebook in the workspace During training, a dataset is created with accuiacy and loss metrics for each epoch.
You need to configure model tracking with MLflow to log the dataset created during the training.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：


질문 # 360
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：


질문 # 361
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

정답：

설명：

1 - Vary the length of frequency brands between modeling epochs.
2 - Standardize to mono audio clips.
3 - Use an Inverse Fourier..


질문 # 362
......

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