Free PDF Quiz 2026 Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure Newest Test Centres

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
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
Explore data and train models35-40%- Optimize model performance
  • 1. Evaluate models
  • 2. Improve accuracy and performance
- Run experiments and train models
  • 1. Use automated machine learning
  • 2. Perform hyperparameter tuning
  • 3. Track experiments
- Prepare data for modeling
  • 1. Manage datasets and datastores
  • 2. Ingest and transform data
Prepare a model for deployment20-25%- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
- 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. Configure security and access
  • 2. Manage compute resources
  • 3. Configure workspace resources

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q213-Q218):

NEW QUESTION # 213
You create an Azure Machine Learning pipeline named pipeline 1 with two steps that contain Python scnpts. Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline 1 and run the pipeline again.
You need to ensure the new run of pipeline 1 fully processes the updated content.
Solution: Change the value of the compute.target parameter of the PythonScriptStep object in the two steps.
Does the solution meet the goal'

Answer: B


NEW QUESTION # 214
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
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.

Answer:

Explanation:

Explanation:


NEW QUESTION # 215
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_dat
a. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample.data folder into a Pandas data frame. You need to complete the Python SDK v2 code to use the MLTaWe folder as the materialization blueprint. How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 216
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 217
You deploy a model in Azure Container Instance.
You must use the Azure Machine Learning SDK to call the model API.
You need to invoke the deployed model using native SDK classes and methods.
How should you complete the command? To answer, select the appropriate options in the answer areas.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({'data': [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding='utf8')
prediction = service.run(input_data=test_sample)
print(prediction)
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment


NEW QUESTION # 218
......

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