This course provide overall 540 unique questions for your exam preparation. There are no duplicated questions. All questions are multiple choice with one or several correct answer. You will get the information of how many answers are correct for each question as in the real exam.
There are 5 test exams for you. Each has 60 questions as the real exam has.
There sixth exam test contains 240 questions. You should do this test, after solving the first 5 tests with a very good score
You will learn about the following contents as they can be covered by the exam:
Exam Topics
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
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