Use this quick start guide to collect all the information about Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-100 Microsoft Designing and Implementing a Data Science Solution on Azure exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCA Azure Data Scientist certification exam.
The Microsoft Designing and Implementing a Data Science Solution on Azure certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure Data Scientist Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCA Azure Data Scientist.
Microsoft Designing and Implementing a Data Science Solution on Azure Exam Summary:
| Exam Name | Microsoft Certified - Azure Data Scientist Associate |
| Exam Code | DP-100 |
| Exam Price | $165 (USD) |
| Duration | 120 mins |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Books / Training | DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Schedule Exam | Pearson VUE |
| Sample Questions | Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions |
| Practice Exam | Microsoft DP-100 Certification Practice Exam |
Microsoft DP-100 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Design and prepare a machine learning solution (20-25%) |
|
| Design a machine learning solution |
- Identify the structure and format for datasets - Determine the compute specifications for machine learning workload - Select the development approach to train a model |
| Create and manage resources in an Azure Machine Learning workspace |
- Create and manage a workspace - Create and manage datastores - Create and manage compute targets - Set up Git integration for source control |
| Create and manage assets in an Azure Machine Learning workspace |
- Create and manage data assets - Create and manage environments - Share assets across workspaces by using registries |
Explore data, and run experiments (20-25%) |
|
| Use automated machine learning to explore optimal models |
- Use automated machine learning for tabular data - Use automated machine learning for computer vision - Use automated machine learning for natural language processing - Select and understand training options, including preprocessing and algorithms - Evaluate an automated machine learning run, including responsible AI guidelines |
| Use notebooks for custom model training |
- Use the terminal to configure a compute instance - Access and wrangle data in notebooks - Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute - Retrieve features from a feature store to train a model - Track model training by using MLflow - Evaluate a model, including responsible AI guidelines |
| Automate hyperparameter tuning |
- Select a sampling method - Define the search space - Define the primary metric - Define early termination options |
Train and deploy models (25-30%) |
|
| Run model training scripts |
- Consume data in a job - Configure compute for a job run - Configure an environment for a job run - Track model training with MLflow in a job run - Define parameters for a job - Run a script as a job - Use logs to troubleshoot job run errors |
| Implement training pipelines |
- Create custom components - Create a pipeline - Pass data between steps in a pipeline - Run and schedule a pipeline - Monitor and troubleshoot pipeline runs |
| Manage models |
- Define the signature in the MLmodel file - Package a feature retrieval specification with the model artifact - Register an MLflow model - Assess a model by using responsible AI principles |
| Deploy a model |
- Configure settings for online deployment - Deploy a model to an online endpoint - Test an online deployed service - Configure compute for a batch deployment - Deploy a model to a batch endpoint - Invoke the batch endpoint to start a batch scoring job |
Optimize language models for AI applications (25-30%) |
|
| Prepare for model optimization |
- Select and deploy a language model from the model catalog - Compare language models using benchmarks - Test a deployed language model in the playground - Select an optimization approach |
| Optimize through prompt engineering and Prompt flow |
- Test prompts with manual evaluation - Define and track prompt variants - Create prompt templates - Define chaining logic with the Prompt flow SDK - Use tracing to evaluate your flow |
| Optimize through Retrieval Augmented Generation (RAG) |
- Prepare data for RAG, including cleaning, chunking, and embedding - Configure a vector store - Configure an Azure AI Search-based index store - Evaluate your RAG solution |
| Optimize through fine-tuning |
- Prepare data for fine-tuning - Select an appropriate base model - Run a fine-tuning job - Evaluate your fine-tuned model |
To ensure success in Microsoft MCA Azure Data Scientist certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100) exam.