DP-100최신덤프공부자료덤프자료로Designing and Implementing a Data Science Solution on Azure시험패스가능

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Microsoft DP-100 Exam Syllabus Topics:

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

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최신 Microsoft Azure DP-100 무료샘플문제 (Q490-Q495):

질문 # 490
You are producing a multiple linear regression model in Azure Machine learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting elective feature engineering on all the data.
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：


질문 # 491
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답：

설명：

Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298


질문 # 492
You have a feature set containing the following numerical features: X, Y, and Z.
The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

정답：

설명：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation


질문 # 493
You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.
Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.
You need to produce the distribution.
Which type of distribution should you produce?

정답：B

설명：
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test
https://en.wikipedia.org/wiki/Student%27s_t-test


질문 # 494
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:

Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values


질문 # 495
......

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