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Microsoft DP-100 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Data Science Solution on Azure
Exam Number:DP-100
Exam Format:Drag and Drop, Interactive Tasks, Case Study, Lab, Multiple Choice
Real Exam Qty:40-60
Exam Duration:100 minutes
Passing Score:700/1000
Certificate Validity Period:1 year
Available Languages:Chinese (Simplified), French, Spanish, German, English, Japanese, Portuguese, Korean
Exam Price:$165 USD
Related Certifications:Microsoft Certified: Azure Data Scientist Associate
Sample Questions:Microsoft DP-100 Sample Questions
Exam Way:Online proctored exam or test center delivery through Pearson VUE.
Pre Condition:Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/

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Microsoft DP-100 exam is designed for data scientists, data engineers, and machine learning engineers who want to validate their skills by designing and implementing data science solutions on Azure. DP-100 exam measures the candidate's ability to design and implement data processing, data storage, and data analysis solutions using Azure services. Passing DP-100 exam will provide the candidates with the Microsoft Certified: Azure Data Scientist Associate certification.

The DP-100 certification is aimed at data scientists, data engineers, and other professionals who work with data. It is ideal for those who want to demonstrate their expertise in designing and implementing data science solutions on Azure. Candidates will need to have a solid understanding of data science concepts, as well as experience working with Azure data services.

Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Exam is a certification exam that focuses on assessing the skills and knowledge required to design and implement data science solutions using Azure technologies. DP-100 exam is ideal for professionals who want to demonstrate their expertise in the field of data science and enhance their career prospects.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

Answer:

Explanation:

Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd


NEW QUESTION # 205
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:


NEW QUESTION # 206
Hotspot Question
You create an experiment in Azure Machine Learning Studio。
You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes.
You must increase the number of training examples for class 1 to 4,000 by using data rows.
You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 207
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Service (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
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:


NEW QUESTION # 208
You write five Python scripts that must be processed in the order specified in Exhibit A - which allows the same modules to run in parallel, but will wait for modules with dependencies.
You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done.
Which Python code segment should you use?

Answer: D

Explanation:
The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step


NEW QUESTION # 209
......

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