DP-100 Prüfungsguide: Designing and Implementing a Data Science Solution on Azure & DP-100 echter Test & DP-100 sicherlich-zu-bestehen

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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Prüfungsfragen mit Lösungen (Q322-Q327):

322. Frage
You need to modify the inputs for the global penalty event model to address the bias and variance issue.
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.

Antwort:

Begründung:

1 - Select the behavior data.
2 - Add a K-Means clustering module with 10 clusters
3 - Perform a primary Component Analysis (PCA)


323. Frage
You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files.
Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.
You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.
Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source 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.

Antwort:

Begründung:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets


324. Frage
You have an Azure Machine Learning workspace
You plan to use the Azure Machine Learning SDK for Python v1 to submit a job to run a training script.
You need to complete the script to ensure that it will execute the training script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Antwort:

Begründung:


325. Frage
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
> > >
> > > from sklearn.model_selection import KFold
> > > X = np.array([[1, 2], [3, 4] , [1, 2], [3, 4]] )
> > > y = np.array([1, 2, 3, 4])
> > > kf = KFold(n_splits=2)
> > > kf.get_n_splits(X)
2
> > > print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
> > > for train_index, test_index in kf.split(X):
print( " TRAIN: " , train_index, " TEST: " , test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html


326. Frage
Your Azure Machine Learning workspace has a dataset named real_estate_dat a. A sample of the data in the dataset follows.

You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py


327. Frage
......

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