Azure DP-100 is the Data Science Certification from Microsoft for all the people who are interested in Data Handling. The learning is self-paced and flexible. Once you get certified, you can work on Azure models, build and operate the models, track the experiments, build pipelines, and tune hyperparameters in the Azure way.
The topics covered in this blog are:
- What is Azure Data Scientist Certification?
- Why You Should Learn Data Science?
- Who This Certification is For?
- Benefits of DP-100 Certification
- DP-100 Exam Detail
- DP-100 Exam Skills Measured
- How to Register for Azure Dp 100 Exam
- Prerequisites for DP-100 Certification
- DP 100 Study Guide
- DP-100 Exam Retake Policy
- DP 100 Exam Day Tips
What is Azure Data Scientist Certification?
With the DP 100 Microsoft Azure Data Scientist Certification, you will be able to work on machine learning workloads using Azure Machine Learning Service and apply your knowledge of data science and machine learning.
In addition, it involves planning and creating a suitable working environment for data science workloads on Azure, executing data experiments, and training predictive machine learning models.
Are you new to the Azure cloud? Do check out our blog post on the Azure Certification Path 2022 and choose the best certification for you.
Why You Should Learn Data Science?
In IT industries, a lot of raw data is generated daily. Therefore, an IT industry needs a dedicated team to work on this raw data, evaluate it and plot the data to make inferences. Moreover, the team is required to apply the Machine Learning algorithm to make predictions.
Also, there is a huge gap between demand and supply for Data Scientists and right now Data Scientist jobs are trending in the IT industry.
Who This Certification is For?
The certificate is applicable to,
- Candidates who are interested in Machine Learning Like Scikit-Learn, PyTorch, and Tensorflow.
- Some knowledge of Data handling and Artificial Intelligence
- People good at statistics
- The Data Scientist who prepares data, trains models, and evaluates competing models, but hasn’t used Azure to do so before.
Check Out: Azure Data Factory Interview Questions
Benefits of DP-100 Certification
- Create different workspaces for running machine learning models.
- Using Machine Learning to implement and run Machine Learning on Azure.
- Planning, running data experiments, training predictive, and creating a working environment for Data science workloads on Azure.
- AI/ML Data Scientist Jobs are trending in the IT industry.
- The IT industry is flooded with jobs for candidates who have basic knowledge about using Microsoft Azure Services. Candidates having Microsoft certification has a good job opportunity.
- The certificate is proof that your skills have been validated.
DP-100 Exam Details
Exam DP-100: Designing and Implementing a Data Science Solution on Azure
Multiple Choice Examination
Number of Questions
40 - 60 Questions
English, Japanese, Korean, and Simplified Chinese
DP-100 Exam Skills Measured
Manage Azure resources for machine learning
Run experiments and train models
Deploy and operationalize machine learning solutions
Implement responsible machine learning
How to Register for Azure Dp 100 Exam
You can register for the DP-100 Exam (Designing And Implementing A Data Science Solution On Azure) by going to the Official Microsoft Page.
Prerequisites For DP 100 Exam
- An understanding of Machine Learning, Artificial Intelligence, and Data Science.
- Candidate having existing knowledge of Python and Machine Learning. Experience of working on it for at least 3-6 months makes it easy to prepare for the exam.
- Basic knowledge of Azure data fundamentals
- DP-900 Certification (not mandatory)
By taking the DP-900 certification exam, it would be easy to learn the Basic knowledge of Azure. Moreover, it will be easy to crack the DP-100 certification exam.
Knowledge of Machine learning helps to make sense of the codes, and answer Machine Learning questions during the exam.
DP 100 Study Guide
Manage Azure resources for machine learning (25-30%)
Create an Azure Machine Learning workspace
- Create an Azure Machine Learning workspace
- Configure workspace settings
- Manage a workspace by using Azure Machine Learning studio
Manage data in an Azure Machine Learning workspace
- Select Azure storage resources
- Register and maintain datastores
- Create and manage datasets
Manage compute for experiments in Azure Machine Learning
- Determine the appropriate compute specifications for a training workload
- Create compute targets for experiments and training
- Configure Attached Compute resources including Azure Databricks
- Monitor compute utilization
Implement security and access control in Azure Machine Learning
- Determine access requirements and map requirements to built-in roles
- Create custom roles
- Manage role membership
- Manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment
- Create compute instances
- Share compute instances
- Access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace
- Create an Azure Databricks workspace
- Create an Azure Databricks cluster
- Create and run notebooks in Azure Databricks
- Link and Azure Databricks workspace to an Azure Machine Learning workspace
Run experiments and train models (20-25%)
Create models by using the Azure Machine Learning designer
- Create a training pipeline by using Azure Machine Learning designer
- Ingest data in a designer pipeline
- Use designer modules to define a pipeline data flow
- Use custom code modules in designer
Run model training scripts
- Create and run an experiment by using the Azure Machine Learning SDK
- Configure run settings for a script
- Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
- Run a training script on Azure Databricks compute
- Run code to train a model in an Azure Databricks notebook
Generate metrics from an experiment run
- Log metrics from an experiment run
- Retrieve and view experiment outputs
- Use logs to troubleshoot experiment run errors
- Use MLflow to track experiments
- Track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models
- Use the Automated ML interface in Azure Machine Learning studio
- Use Automated ML from the Azure Machine Learning SDK
- Select pre-processing options
- Select the algorithms to be searched
- Define a primary metric
- Get data for an Automated ML run
- Retrieve the best model
Tune hyperparameters with Azure Machine Learning
- Select a sampling method
- Define the search space
- Define the primary metric
- Define early termination options
- Find the model that has optimal hyperparameter values
Deploy and operationalize machine learning solutions (35-40%)
Select compute for model deployment
- Consider security for deployed services
- Evaluate compute options for deployment
Deploy a model as a service
- Configure deployment settings
- Deploy a registered model
- Deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
- Consume a deployed service
- Troubleshoot deployment container issues
Manage models in Azure Machine Learning
- Register a trained model
- Monitor model usage
- Monitor data drift
Create an Azure Machine Learning pipeline for batch inferencing
- Configure a ParallelRunStep
- Configure compute for a batch inferencing pipeline
- Publish a batch inferencing pipeline
- Run a batch inferencing pipeline and obtain outputs
- Obtain outputs from a ParallelRunStep
Publish an Azure Machine Learning designer pipeline as a web service
- Create a target compute resource
- Configure an inference pipeline
- Consume a deployed endpoint
Implement pipelines by using the Azure Machine Learning SDK
- Create a pipeline
- Pass data between steps in a pipeline
- Run a pipeline
- Monitor pipeline runs
Apply ML Ops practices
- Trigger an Azure Machine Learning pipeline from Azure DevOps
- Automate model retraining based on new data additions or data changes
- Refactor notebooks into scripts
- Implement source control for scripts
Implement responsible machine learning (5-10%)
Use model explainers to interpret models
- Select a model interpreter
- Generate feature importance data
Describe fairness considerations for models
- Evaluate model fairness based on prediction disparity
- Mitigate model unfairness
Describe privacy considerations for data
- Describe principles of differential privacy
- Specify acceptable levels of noise in data and the effects on privacy
DP-100 Exam Retake Policy
The DP-100 exam retake policy is as follows:
- If a candidate fails on the first attempt, they must wait for 24 hours before retaking the exam.
- If a candidate again fails on the second attempt, then the candidate will have to wait for 14 days.
- A candidate will be given a maximum of five attempts to retake an exam in a year.
DP-100 Exam Day Tips
- Schedule the exam a week or two in advance.
- Set up a certification profile (if you haven’t already)
- Verify personal information and payment method
- It is recommended to take the exam online.
- The test-taking space should be quiet.
- Do not read the question aloud, this may lead to disqualification.
- Before taking photos, remove any paper, pencils, external keyboard, etc, from sight in compliance with test rules.
- You are not allowed to write, you can use a virtual notepad during the test.
- Be mindful of your eyes during your test. You will be recorded by your front-facing camera during your test, and a Pearson VUE proctor will monitor you throughout the session. The lack of attention to your computer screen during the test may lead to suspicions that you are cheating.
- If you stare off into the distance while thinking, it is best to resist that urge during your exam.
There is a huge demand for data scientists, machine learning experts, Artificial intelligence, etc. and the future of Data science is defined by large firms such as Microsoft, Amazon, Databricks, and google. Due to this reason, it makes sense to get certification from any of these big firms.
The Azure DP-100 certification is just the beginning of your learning journey toward Machine Learning, not the end. Once you get the certification it is on you to put that skills into good use whether it is a job or for your personal project.