Free PDF 2025 DP-100: Professional Latest Designing and Implementing a Data Science Solution on Azure Test Materials

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Data science is a lucrative career option and you can explore it without any hassle if you’ve managed to taste success in the Microsoft DP-100 exam, which is known to create a skilled workforce of Azure data scientists. However, a great outcome in such a test will only come if the aspirant is referring to the updated and recent study resources like the official courses provided by the exam vendor itself.

The DP-100 Exam covers a range of topics, including data preparation, modeling, and deployment. Candidates will need to demonstrate their ability to work with various Azure data services, such as Azure Machine Learning, Azure Databricks, and Azure HDInsight. They will also need to show their proficiency in programming languages such as Python and R.

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Microsoft DP-100 certification exam is a comprehensive exam that covers a wide range of topics related to data science solutions on Azure. DP-100 exam consists of multiple-choice questions and requires the candidate to demonstrate their knowledge and understanding of data science concepts and Azure data science solutions. DP-100 Exam is designed to test the candidate’s ability to design and implement data science solutions on Azure.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q125-Q130):

NEW QUESTION # 125
You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run. Which Python code segment should you use?

Answer: C

Explanation:
Explanation
The get_output method on automl_classifier returns the best run and the fitted model for the last invocation.
Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.
In [ ]:
best_run, fitted_model = local_run.get_output()
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-mach

NEW QUESTION # 126
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.
You must submit an experiment that runs this script in the Azure Machine Learning workspace.
The following compute resources are available:
a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software a Compute Instance named in the workspace with 2 CPUs and 8 GB of memory an Azure Machine Learning compute target named with eight CPU-based nodes an Azure Machine Learning compute target named with four CPU and GPU-based nodes You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.
Which resources should the data scientist use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

NEW QUESTION # 127
You have machine learning models produce unfair predictions across sensitive features.
You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness.
You need to select a post-processing technique and model type.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 128
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

You need to complete the Python code to log the table.
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:

Explanation

NEW QUESTION # 129
You publish a batch inferencing pipeline that will be used by a business application.
The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
You need to identify the information required in the REST request and returned as a response from the published pipeline.
Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: JSON containing an OAuth bearer token
Specify your authentication header in the request.
To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.
Box 2: JSON containing the experiment name
Add a JSON payload object that has the experiment name.
Example:
rest_endpoint = published_pipeline.endpoint
response = requests.post(rest_endpoint,
headers=auth_header,
json={"ExperimentName": "batch_scoring",
"ParameterAssignments": {"process_count_per_node": 6}})
run_id = response.json()["Id"]
Box 3: JSON containing the run ID
Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification

NEW QUESTION # 130
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FREE PDF 2025 DP-100: PROFESSIONAL LATEST DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE TEST MATERIALS

Free PDF 2025 DP-100: Professional Latest Designing and Implementing a Data Science Solution on Azure Test Materials