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Exam DP-100: Designing and Implementing a Data Science Solution on Azure
Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.
Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.
A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.
Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate
Skills Covered
To nail DP-100, you will need to scrutinize the below-mentioned areas:
- Set up Azure ML Workspace
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
- Deploy and Consume Models
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
- Manage and Optimize Models
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
- Execute Experiments & Train Models
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
2. Train Models & Run Experiments (25-30%):
- 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.
- 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.
- 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.
- 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.
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The benefit in Obtaining the DP-100 Exam Certification
- After completion of Microsoft Certified Azure Data Scientist Associate Certification candidates receive official confirmation from Microsoft that you are now fully certified in their chosen field. This can be now added to their CV, cover letters and job applications.
- Becoming Microsoft Certified Azure Data Scientist Associate means one thing you are worth more to the company and therefore more to yourself in the form of an upgraded pay package. On average a Microsoft Certified Azure Data Scientist Associate member of staff is estimated to be worth 30% more to a company than their uncertified professionals.
- Candidates will get in-depth knowledge by completing the courses along with the access to revision materials for 6 months upon completion means they will have a wider skill set when it comes to the various technologies and systems than an uncertified professional. Certified Professional in this particular skill set is 74% more efficient when it comes to completing their tasks in a timely well-executed manner.
- When Candidates applying for a job or looking to promotion in their current position, a Microsoft Certified Azure Data Scientist Associate certification in the field in which Candidates are applying will put you at the top of the list and make them a desirable candidate for employers.
- Organization owners invest a lot in their employees when it comes to their training with the goal of making them quicker, more efficient, and more knowledgeable about their role. Certified Professional will reduce the time he spends on tasks, meaning he can get more done this could help reduce company downtime when repairing faults on a system or fixing hardware problems.
If you are still confused about how to prepare for the IT exam, I guess you may have interest in the successful experience of others who have passed the IT exam as well as get the IT certification with the help our DP-100 Korean learning material: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version). I am so proud to tell you that we have received thousands of letters of thanks from our customers in many different countries, which are the best proofs to show everyone how useful our DP-100 Korean practice test are. And now, our company has become the strongest one in the IT field, and the most crucial reason about why we can be so success is that we always make every endeavor to satisfy our customers, and we assure you that all of the contents in our DP-100 Korean learning material: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) are essence for the IT exam, our actual lab questions equal to the most useful and effective study resources. Now, I would like to show you some strong points of our DP-100 Korean study guide.
Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 2: Explore data and train models | 35-40% | - Prepare data for modeling
|
| Topic 3: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Topic 4: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|






