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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Passing Score: | 700/1000 |
| Available Languages: | Spanish, French, Russian, German, Korean, Portuguese (Brazil), Japanese, English, Simplified Chinese |
| Exam Format: | Drag and drop, Multiple choice, Hands-on lab tasks, Multiple response, Case studies |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Exam Price: | USD 165 (varies by region) |
| Exam Duration: | 100-120 |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Azure Machine Learning Documentation Microsoft Learn - DP-100 Learning Path |
| Exam Registration: | Pearson VUE Registration Official Microsoft Certification Page |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers (Pearson VUE) |
| Pre Condition: | No mandatory prerequisites. Recommended familiarity with Python, machine learning concepts, and Azure fundamentals. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/ |
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One of the key benefits of the DP-100 certification is that it demonstrates a professional's ability to work with a variety of Azure data services, including Azure Machine Learning, Azure Databricks, and Azure Stream Analytics. These services are critical for building data science solutions that can help organizations extract insights and drive business value. By earning the DP-100 Certification, data professionals can showcase their expertise in these areas and differentiate themselves from other candidates in the job market.
NEW QUESTION # 63
You set up a machine learning workflow as an automated process. You have an Owner role in an Azure subscription that contains the Azure Machine Learning workspace.
You must set up an authentication method that allows an automated process to authenticate to the workspace without requiring user interaction.
You need to set up the authentication for the Azure Machine Learning workspace.
Which three authentication steps should you perform in sequence? To answer, move the appropriate authentication steps from the list of authentication steps to the answer area and arrange them in the correct order.
NOTE More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
NEW QUESTION # 64 
You need to obtain the output from the pipeline execution. Where will you find the output?
Answer: A
Explanation:
Explanation
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and
'summary_only'.
'append_row' - All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parall
NEW QUESTION # 65
You manage an Azure Machine Learning workspace. You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job.
You need to create a time-based schedule with recurrence pattern.
Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
NEW QUESTION # 66
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or 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.
Answer:
Explanation:
Explanation
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION # 67
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment You need to complete the script for the experiment How should you complete the code? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
NEW QUESTION # 68
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