Question A1.1) Create an Azure Machine Learning workspace
]]>Question A1.2) Configure workspace settings
]]>Question A1.3) Manage a workspace by using Azure Machine Learning Studio
]]>Question A2.1) Register and maintain data stores
]]>Question A2.2) Create and manage datasets
]]>Question A3.1) Create a compute instance
]]>Question A3.2) Determine appropriate compute specifications for a training workload
]]>Question A3.3) Create compute targets for experiments and training
]]>Question B1.1) Create a training pipeline by using Azure Machine Learning designer
]]>Question B1.2) Ingest data in a designer pipeline
]]>Question B1.3) Use designer modules to define a pipeline data flow
]]>Question B1.4) Use custom code modules in designer
]]>Question B2.1) Create and run an experiment by using the Azure Machine Learning SDK
]]>Question B3.1) Log metrics from an experiment run
]]>Question B3.2) Retrieve and view experiment outputs
]]>Question B3.3) Use logs to troubleshoot experiment run errors
]]>Question B4.1) Create a pipeline by using the SDK
]]>Question B4.2) Pass data between steps in a pipeline
]]>Question B.4.3) Run a pipeline
]]>Question C1.1) Use the Automated ML interface in Azure Machine Learning studio
]]>Question C1.2) Use Automated ML from the Azure Machine Learning SDK
]]>Question C1.3) Select scaling functions and pre-processing options
]]>Question C1.4) Determine algorithms to be searched
]]>Question C1.5) Define a primary metric
]]>Question C1.6) Get data for an Automated ML run
]]>Question C1.7) Retrieve the best model
]]>Question C2.1) Select a sampling method
]]>Question C2.2) Define the search space
]]>Question C2.3) Define the primary metric
]]>Question C2.4) Define early termination options
]]>Question C3.1) Select a model interpreter
]]>Question C3.2) Generate feature importance data
]]>Question C4.1) Register a trained model
]]>Question C4.2) Monitor model history
]]>Question C4.3) Monitor data drift
]]>
Your confidence level in the subsection A1) Create an Azure Machine Learning workspace is {ScoreA1} percent.
Your confidence level in the subsection A2) Manage data objects in an Azure Machine Learning workspace is {ScoreA2} percent.
Your confidence level in the subsection A3) Manage experiment compute contexts is {ScoreA3} percent.
Your confidence level for the main section A) Set up an Azure Machine Learning Workspace is {round((sum(ScoreA1.valueNAOK,ScoreA2.valueNAOK,ScoreA3.valueNAOK)/3),0)} percent.
]]>
Your confidence level in the subsection B1) Create models by using Azure Machine Learning Designer is {ScoreB1} percent.
Your confidence level in the subsection B2) Run training scripts in an Azure Machine Learning workspace is {ScoreB2} percent.
Your confidence level in the subsection B3) Generate metrics from an experiment run is {ScoreB3} percent.
Your confidence level in the subsection B4) Automate the model training process is {ScoreB4} percent.
Your confidence level for the main section B) Run Experiments and Train Models is {round((sum(ScoreB1.valueNAOK,ScoreB2.valueNAOK,ScoreB3.valueNAOK,ScoreB4.valueNAOK)/4),0)} percent.
]]>
Your confidence level in the subsection C1) Use Automated ML to create optimal models is {ScoreC1} percent.
Your confidence level in the subsection C2) Use Hyperdrive to tune hyperparameters is {ScoreC2} percent.
Your confidence level in the subsection C3) Use model explainers to interpret models is {ScoreC3} percent.
Your confidence level in the subsection C4) Manage models is {ScoreC4} percent.
Your confidence level for the main section C) Optimize and Manage Models is {round((sum(ScoreC1.valueNAOK,ScoreC2.valueNAOK,ScoreC3.valueNAOK,ScoreC4.valueNAOK)/4),0)} percent.
]]>Question B2.2) Consume data from a data store in an experiment by using the Azure Machine Learning SDK
]]>Question B2.3) Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
]]>Question B2.4) Choose an estimator for a training experiment
]]>Question B4.4) Monitor pipeline runs
]]>Question C2.5) Find the model that has optimal hyperparameter values
]]>Question D1.1) Consider security for deployed services
]]>Question D1.2) Evaluate compute options for deployment
]]>Question D2.1) Configure deployment settings
]]>Question D2.2) Consume a deployed service
]]>Question D2.3) Troubleshoot deployment container issues
]]>Question D3.1) Publish a batch inferencing pipeline
]]>Question D3.2) Run a batch inferencing pipeline and obtain outputs
]]>Question D4.1) Create a target compute resource
]]>Question D4.2) Configure an Inference pipeline
]]>Question D4.3) Consume a deployed endpoint
]]>
Your confidence level in the subsection D1) Create production compute target is {ScoreD1} percent.
Your confidence level in the subsection D2) Deploy a model as a service is {ScoreD2} percent.
Your confidence level in the subsection D3) Create a pipeline for batch inferencing is {ScoreD3} percent.
Your confidence level in the subsection D4) Publish a designer pipeline as a web service is {ScoreD4} percent.
Your confidence level for the main section D) Deploy and Consume Models is {round((sum(ScoreD1.valueNAOK,ScoreD2.valueNAOK,ScoreD3.valueNAOK,ScoreD4.valueNAOK)/4),0)} percent.
]]>
Tips for use:
1. Go to the "Self Assessment" sheet, and mark each "Task / Topic" with your level of confidence.
2. Review the different exam sub-domains to see your level of confidence in each area
3. Review the different exam objective domains to see your level of confidence in each area
4. Study each "Task / Topic" that's not set to "Know Well", then update your confidence level accordingly.
5. Once your self-assessment is at a high Overall Confidence Level, then you are ready to take the exam with confidence!
]]>Happy studying, and good luck on the exam!
Exam DP-100: Designing and Implementing a Data Science Solution on Azure
https://docs.microsoft.com/learn/certifications/exams/dp-100
Have a nice day and see you soon!
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