高質量的DP-100指南|第一次嘗試輕鬆學習並通過考試&可靠的DP-100：Designing and Implementing a Data Science Solution on Azure

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Microsoft DP-100 Exam Syllabus Topics:

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

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最新的 Microsoft Azure DP-100 免費考試真題 (Q50-Q55):

問題 #50
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.
You are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply an Equal Width with Custom Start and Stop binning mode.
Does the solution meet the goal?

答案：A

解題說明：
Use the Entropy MDL binning mode which has a target column.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins


問題 #51
You need to select a feature extraction method.
Which method should you use?

答案：C

解題說明：
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules


問題 #52
You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.
You need to format the data for the Weka environment.
Which module should you use?

答案：A

解題說明：
Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff


問題 #53
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:

Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself.
Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


問題 #54
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.

答案：

解題說明：

Explanation:


問題 #55
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