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.
- 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.
- 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.
Passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is the major requirement for earning the Microsoft Certified: Azure Data Scientist Associate certification. This test measures the ability of the professionals to execute the following technical tasks: setting up an Azure Machine Learning workspace; running experiments & train models; optimizing and handling models; deploying and consuming models.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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: | Multiple select, Case studies, Multiple choice, Drag and drop, Yes/No |
| Available Languages: | Arabic (Saudi Arabia), Korean, French, Chinese (Simplified), Italian, Portuguese (Brazil), Russian, Indonesian (Indonesia), Spanish, German, Chinese (Traditional), Japanese, English |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Data Engineer Associate |
| Certificate Validity Period: | 1 year |
| Passing Score: | 700 |
| Exam Duration: | 100 minutes |
| Exam Price: | $165 USD |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Microsoft Learn Learning Path |
| Exam Registration: | Microsoft Learn Registration
Pearson VUE Scheduling |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 |
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Explore data and run experiments | 20-25% | - Explore and visualize data
- 1. Detect anomalies and outliers
- 2. Profile and validate data
- 3. Identify features and relationships
- Implement pipelines
- 1. Create and publish pipelines
- 2. Build reusable components
- 3. Schedule and monitor pipelines
- 4. Pass data between steps
- Run experiments
- 1. Use automated machine learning
- 2. Define parameters and configurations
- 3. Configure experiment runs
- 4. Track runs with MLflow
|
| Design and prepare a machine learning solution | 20-25% | - Manage compute resources
- 1. Attach and monitor compute
- 2. Create and configure compute targets
- 3. Select environments
- Manage data assets
- 1. Register and manage datastores
- 2. Create and maintain data assets
- 3. Select storage services
- Design a machine learning solution
- 1. Determine dataset structure and format
- 2. Select development approach
- 3. Plan model deployment requirements
- 4. Define compute specifications for workloads
- Manage Azure Machine Learning workspace
- 1. Use developer tools and CLI
- 2. Set up Git integration
- 3. Create and configure workspace
- 4. Work with registries
|
| Train and deploy models | 25-30% | - Train models
- 1. Apply responsible AI principles
- 2. Configure jobs and environments
- 3. Run training scripts
- 4. Use HyperDrive for hyperparameter tuning
- Manage models
- 1. Register and version models
- 2. Interpret models and explain predictions
- 3. Package and validate models
- Deploy models
- 1. Deploy to batch endpoints
- 2. Secure endpoints and manage access
- 3. Deploy to online endpoints
- 4. Configure compute and scaling
- Monitor and maintain models
- 1. Update and retrain models
- 2. Monitor performance and data drift
- 3. Implement MLOps practices
|
| Optimize language models for AI applications | 25-30% | - Evaluate and improve models
- 1. Test and evaluate responses
- 2. Optimize for accuracy and safety
- 3. Apply responsible generative AI
- Implement generative AI solutions
- 1. Apply prompt engineering
- 2. Use Azure AI Foundry
- 3. Build prompt flows
- Optimize with Retrieval Augmented Generation
- 1. Prepare and process data
- 2. Create vector stores and indexes
- 3. Configure Azure AI Search
|
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