Testing Process Details of Microsoft DP-100

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.

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2. Train Models & Run Experiments (25-30%):

• Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
• Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
• Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
• Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.

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3. Models Management and Optimization (20-25%):

• Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
• Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
• Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
• Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx