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Microsoft DP-100 Exam is an important certification for data professionals who want to demonstrate their skills in designing and implementing data science solutions on Azure. DP-100 exam validates the candidate's ability to design and implement machine learning models, process and transform data, and design and implement data science workflows. Designing and Implementing a Data Science Solution on Azure certification is highly valued in the industry and can help data professionals advance their careers and increase their earning potential.

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Microsoft DP-100 Certification Exam is an excellent way for data science professionals to validate their expertise in designing and implementing data science solutions on Microsoft Azure. DP-100 exam is recognized globally and is highly regarded by employers in the field of data science. Designing and Implementing a Data Science Solution on Azure certification is a testament to the candidate's knowledge and expertise in the field of data science and can open up new career opportunities.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q74-Q79):

NEW QUESTION # 74
You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?

A. conda_file
B. properties
C. build
D. image

Answer: D

NEW QUESTION # 75
You use the following code to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?

A. files = run.get_details_with_logs()
B. files = run.get_metrics()
C. files = run.get_properties()
D. files= run.get_file_names()
E. files = run.get_details()

Answer: D

Explanation:
You can list all of the files that are associated with this run record by called run.get_file_names() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

NEW QUESTION # 76
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 77
You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script.
You need to reference a Python class object from the SDK for the statement.
Which class object should you use?

A. Run
B. Workspace
C. ScriptRunConfig
D. Experiment

Answer: A

Explanation:
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory.
Incorrect Answers:
A: A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
B: A ScriptRunConfig packages together the configuration information needed to submit a run in Azure ML, including the script, compute target, environment, and any distributed job-specific configs.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)

NEW QUESTION # 78
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 train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AksWebservice instance.
Set the value of the auth_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?

A. No
B. Yes

Answer: B

Explanation:
Key-based authentication.
Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key- based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.
deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE) Reference:
https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

NEW QUESTION # 79
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