DP-100 Designing and Implementing a Data Science Solution on Azure Exam Prep

By: Daniel Calbimonte   |   Updated: 2023-10-19   |   Comments   |   Related: More > Professional Development Certifications

Problem

Data Science is one of the best-paying jobs in the world. Microsoft now offers an official certification in Data Science and to help you get started, this article provides several links to help you get ready for the DP-100: Designing and Implementing a Data Science Solution on Azure exam.

Solution

This article will highlight some suggestions to pass the DP-100 exam. We will recommend books, courses, links, and tips about this exam.

What is Data Science on Azure?

Azure is the Microsoft cloud and provides services in the cloud like virtual servers, web pages, big data, APIs, databases, Kubernetes, compute, analysis, etc.

Data Science can also be handled in Azure. Data science is the field that studies how to process and extract knowledge from the data. In Data Science, you need to use algorithms to get insights from the data. To extract the data, you will need to collect, clean, and analyze the data. It is also important to have a good knowledge of statistics to handle data.

What is the DP-100 Exam About?

The exam is about Azure Machine Learning Services: how to design and prepare ML solutions, how to explore data, how to deploy models, and how to train models. Basically, it is an exam about machine learning.

Is the Exam Difficult?

Yes, it is a complex topic. If you are an expert in Azure Machine Learning, you may have less difficulties than a non-experienced person. Take a look at the study guide, videos, and courses. If everything seems familiar, maybe the exam will not be so difficult. If you do not know Azure or Machine Learning, I recommend taking some basic exams about Azure first before taking this advanced exam.

What is a Passing Score?

The score to pass is 700/1000 (approximately).

Which Books are Recommended for the Exam?

The following books will be helpful:

• Azure Data Scientist Associate Certification Guide: A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
• Designing and Implementing a Data Science Solution on Azure Practice Questions and Exam Tests: Microsoft DP-100 Exam Guidebook And Updated Questions
• Microsoft Certified Azure Data Scientist Associate DP-100 : Exam DP-100: Designing and Implementing a Data Science Solution on Azure First Edition Conclusion

Could You Please Send Me the Study Guide?

Here is the official Microsoft Study Guide link: DP 100 Study guide.

What Courses Would You Recommend I Take to Pass the Exam?

These courses can help you:

• DP-100 Part 1 - Preparation
• DP-100 Microsoft Azure Data Scientist Complete Exam Prep
• Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate
• Designing and Implementing a Data Science Solution on Azure
• Explore the Azure Machine Learning workspace

Would You Recommend Some Links for the Exam?

Yes, here are some links to study for the exam:

Design a Machine Learning Solution

• Devise a plan for a machine learning solution.
• Determine the required computational specifications for training workloads.
• Outline the deployment prerequisites for the model.

Choose the Development Methodology for Model Construction or Training

• Administer an Azure Machine Learning workspace.
• Establish an Azure Machine Learning workspace.
• Govern the workspace through developer tools for interaction.
• Enable Git integration for source control.

Supervise Data Within an Azure Machine Learning Workspace

• Pick Azure Storage resources.
• Register and maintain data repositories.
• Generate and oversee data assets.

Oversee Compute Resources for Experiments in Azure Machine Learning

• Create computing targets for training and experiments.
• Choose an environment for a specific machine learning use case.
• Configure linked computing resources, such as Azure Databricks and Azure Synapse Analytics.
• Monitor compute resource usage.

Explore Data and Train Models

• Investigate data using data assets and data repositories.
• Load and transform data.
• Analyze data using Azure Data Explorer.
• Employ differential privacy techniques.

Construct Models Using the Azure Machine Learning Designer

• Establish a training pipeline.
• Utilize data assets within the designer.
• Employ designer components to define data flow within a pipeline.
• Utilize custom code components within the designer.
• Evaluate the model, including adherence to responsible AI guidelines.

Utilize Automated Machine Learning to Explore Optimal Models

• Apply automated machine learning to tabular data.
• Utilize automated machine learning for computer vision tasks.
• Implement automated machine learning for natural language processing (NLP).
• Choose and comprehend training options, including preprocessing and algorithms.
• Assess an automated machine learning run, considering responsible AI guidelines.

Utilize Notebooks for Custom Model Training

• Develop code within a compute instance.
• Access data within a notebook.
• Track model training using MLflow.
• Train a model using the Python SDK.
• Configure a compute instance through the terminal.

Fine-tune hyperparameters with Azure Machine Learning.

• Select a method for samples.
• Understand the search space.
• Specify the primary metric.
• Establish early termination options.

Prepare Models for Deployment

• Execute model training scripts:
  • Configure job run configurations for scripts.
  • Set up the compute environment for a job run.
  • Ingest data from a data asset during a job.
  • Initiate a script as a job using Azure Machine Learning.
  • Employ MLflow to record metrics from a job run.
  • Utilize logs for troubleshooting job run errors.
  • Configure an environment for a job run.
  • Define parameters for a job.

• Implement training pipelines:
  • Create pipelines.
  • Send data between steps within a pipeline.
  • Execute and schedule a pipeline.
  • Monitor pipeline executions.
  • Establish custom components.
  • Employ component-based pipelines.

• Manage models within Azure Machine Learning:
  • Understand the output of MLflow models.
  • Identify the right framework for packaging models.
  • Evaluate a model using responsible AI guidelines.

Deploy and Retrain a Model

• Deploy a model:
  • Configure real-time deployments.
  • Deploy models to a real-time endpoint.
  • Deploy models to a batch endpoint.
  • Test real-time deployed services.
  • Initiate a batch endpoint for batch scoring.

Implement Machine Learning Operations (MLOps) Practices:

• Trigger an Azure ML pipeline using Azure DevOps or GitHub.
• Automate model retraining using new data additions or data changes.
• Define triggers for event-based retraining.

Next Steps

• To learn more about Machine Learning, refer to these links:
  • Basics of Machine Learning with SQL Server 2017 and R
  • Introduction to SQL Server Machine Learning Services with Python
  • Introduction to Microsoft Azure Machine Learning

About the author

Daniel Calbimonte is a Microsoft SQL Server MVP, Microsoft Certified Trainer and 6-time Microsoft Certified IT Professional. Daniel started his career in 2001 and has worked with SQL Server 6.0 to 2022. Daniel is a DBA as well as specializes in Business Intelligence (SSIS, SSAS, SSRS) technologies.

This author pledges the content of this article is based on professional experience and not AI generated.

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