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By passing the DP-100 certification exam, data scientists and data engineers can demonstrate their expertise in designing and implementing data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification can help candidates stand out in a competitive job market and increase their earning potential. It can also provide a foundation for further Microsoft certifications, such as the AI-100 (Designing and Implementing an Azure AI Solution) certification.
The DP-100 Certification Exam is part of Microsoft's Data and AI Certification track and is a crucial step for data scientists who wish to advance their careers. It covers a range of topics, including data preparation, model training and deployment, and machine learning algorithms. It also requires candidates to have hands-on experience with Azure services such as Azure Machine Learning, Azure Databricks, and Azure SQL Database.
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As for the exam format, there are usually about 40-60 multiple-choice, active screen, best answer, hot area, build lists, labs, mark review and other question styles. You are expected to complete them in 3 hours. The passing score is 700 out of 1000 maximum possible points. The test is available in the following languages: English, Japanese, Korean, and Chinese (Simplified). To schedule your test, you should first pay the registration fee of $165 and go to the Pearson VUE platform to create an account. Once you log in, choose the “Proctored Exams” section and enter ‘DP-100' as the exam code. Follow the prompts to complete registration. At this stage, you can choose to take your test offline or online. Notice, though, that if you don't succeed on the first try, you'll be required to pay the enrollment fee for the next attempt as well.
NEW QUESTION # 59
You use Azure Machine Learning to train a machine learning model.
You use the following training script in Python to perform logging:
You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyperparameter tuning to optimize.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 60
You are building a recurrent neural network to perform a binary classification.
You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.
You need to analyze model performance.
You need to identify whether the classification model is overfitted.
Which of the following is correct?
Answer: B
Explanation:
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
Reference:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
NEW QUESTION # 61
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 # 62
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml
NEW QUESTION # 63
A coworker registers a datastore in a Machine Learning services workspace by using the following code:
You need to write code to access the datastore from a notebook.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
NEW QUESTION # 64
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