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Microsoft DP-100 Exam Syllabus Topics:

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

>> DP-100熱門考題 <<

DP-100更新 - DP-100試題

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最新的 Microsoft Azure DP-100 免費考試真題 (Q240-Q245):

問題 #240
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：


問題 #241
You need to build a feature extraction strategy for the local models.
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：


問題 #242
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

答案：

解題說明：

Explanation
Create Scatterplot
Summarize Data
Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values


問題 #243
You need to evaluate the potential risk of exposing personal information based on the values of epsilon and delta for differential privacy. You create a privacy report.
What does an epsilon value greater than one represent?

答案：A

解題說明：
When epsilon is greater than one, it indicates that the privacy guarantee is weakened, and there is a higher risk of revealing sensitive or personal information from the data used in the report. A larger epsilon allows for more noise in the data, which can reduce privacy protection. Typically, smaller values of epsilon are preferred to provide stronger privacy guarantees.


問題 #244
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma- separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:


You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

答案：C

解題說明：
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml


問題 #245
......

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