Certification Overview: Sections & Sub-Topics

Question 1

Topics Cover in Azure DS Cert.

( 4 )

Answer 1

Deploy & Operationalize ML Solutions (35 - 40%)

• Model deployment

Run Experiments & Train Models (20 - 25%)

• Build & Train Models

Manage Azure Resources for ML (25 - 30%)

• Set up & Manage an Azure Workspace

Implement Responsible ML (5 - 10%)

• Privacy, Feature Imp., Fairness

Question 2

Subtopics: Deploy & Operationalize ML

(35 - 40%)

( 7 )

Answer 2

Understanding the genreal steps in deployment:

• select the compute, register the model, deploy the models, schedule runs/jobs, trigger a pipeline run
• perform these functions in SDK
• publish these as web services

Select Compute

• security for deployment
• evaluate compute options

Manage Models in Azure ML

• register & monitor models
• monitor data drift

Deploy Model as a Service

• configure deployment settings
• deploy models (Databricks built & other models)
• troubleshoot container issues
• consume deployed service

Create a Pipeline for Batch Inferencing (schedule runs)

• configure parallelRunStep, obtain outputs
• configure batch compute
• publish batch
• run batch pipeline

Apply ML Ops Practices

• trigger a pipeline
• automate model retraining with new data
• refactor notebooks into scripts & implement source controls

Publish Designer Pipeline as a Web Service

Implment Pipelines in SDK

Question 3

Subtopics: Run & Train Models

(20 - 25 %)

( 5 )

Answer 3

Gerenal Overview:

• create, run, generate metrics, optimize, hyperparameter tuning

Create Models Using Designer

• create pipeline
• ingest data
• define data flow
• use custom code modules in designer

Run Model Training Scripts

• consume data & run with SDK
• configure run setting
• run script & train model in Databricks

Generate Metrics for Experiment Run

• log experiment metrics
• retrieve and view outputs
• troubleshoot run errors with logs
• use MLflow to track experiments
• track experiments in Databricks

Optimze Models w/ Automated ML

• use Automated ML interface in Azure Learning Studio
• use Automated ML in SDK
• pre-processing & algorithmn options
• primary metrics
• retrieve best model and get data from automated run

Hyperparameter Tuning

• select sampling method
• define search space & primary metric
• define early termination options
• define model with optimal hyperpatameter values

Question 4

Subtopics: Manage Azure Resources for ML

(workspace stuff)

(25 - 30%)

( 6 )

Answer 4

Create Workspace

• creare, configure, & manage workspace

Manage Data in Workspace

• seelect storage resources and register datastores
• create & manage datasets

Manage Compute for Experiments

• determine appropriate compute ancreate compute targets
• configure & monitor compute

Implement Security & Access Controls

• manage & create roles
• manage credentials
• determine access requirements and map to user roles

Set Up Development Environment

• acess Azure ML from other development environments
• create & share compute instances (more other environ.)

Set Up Databricks Workspace

• create a DB workspace & cluster
• link DB workspace to Azure ML workspace
• create & run notesbooks

Question 5

Subtopics: ML Ops

(5 - 10%)

( 3 )

Answer 5

Use Experiments to Interpret Models

• use model explainer
• generate feature importance data

Describe Fariness Cnsiderations for Models

• evaluate model fairness
• mitigate model unfairness

Describe Privacy Considerations for Data

• principals of differential privacy
• specify acceptable levels of noise in the data and ites effects on privacy