AI-100 Study Guide

Here's my guidance on studying for the AI-100 Exam.
Microsoft Learning GitHub repository for AI-100 Samples:
https://github.com/MicrosoftLearning/AI-100-Design-Implement-Azure-AISol
General topics to study
- Roles in RBAC for Cognitive Services
- Know about LUIS, QnA, Bing Entity Search
- Statements needed to set up a chatbot within a web app
- Chatbot pricing tiers, # of message per day
- Managing secrets with Key Vault
- Best means of monitoring the compute performance of model
- How to connect to an AKS node with SSH
- Know about the different uses for IoT Edge and its modules
- Logic apps are good for long-running processes
- Which Azure environment can be used for a Skype Bot
- Understand machine learning pipelines and the process of building one: import, tune, evaluate, score
- Many questions related to knowing differences / when-to use between:
- Apache Hive
- Apache Spark
- Apache Kafka
- Hive
- Storm
- ML module to import data to HD Insight
- When to use different Azure data stores: Table, Cosmos DB, Data Lake, HDFS
- Configuring full text search
- Knowing when to use IoT Hub vs Event Hubs
- Event-driven models for data preparation / enrichment
- How to process IoT hub messages as quickly as possible
- What to use to perform OCR
- Understand data bricks vs data factory
- What to use for an in-memory cache and columnar storage
- How to access a cognitive services resource as a web service from a logic app
- How to deploy an AI using AKS
- What to use to have a versioned and logged ML model
- Protect data from deletion in key vault
- Encrypt data in blobs using HSM
- Control access to blobs using SAS and RBAC
- Optimize performance of Cosmos DB with slow queries
- About Bot Analytics
- GDPR compliance
- Hadoop vs spark vs Azure SQL
- Which data storage to use based on different amounts of data (blobs vs sql data warehouse vs sql database vs data lake)
- What to use to talk to on-site SQL Server data
- How to share code with developers and customers
- Limit access top AML using RBAC
- What's needed to access an ML web service
- What type of data store to use based on different types of data (JSON, PDF, images)
- How to audit applicaiton usage
- What to use to ingest data from Kafka streams
- What type of compute infrastructure for different types of applications (image recog, deep learning, ...)
- What service to use to for various purposes: edge anomaly detection, send data into azure, process workflows in azure)
- What to use to capture events from IoT devices for AI, real-time query, low latency
- Ensure data is within valid records with MDM
- What to use to ingest data from various data sources (data factory)
- What to use to write-back configs to IoT devices
- Data access with hybrid connections
- Orchestrationg phases of experiments
- How to build an ML pipeline
References
Here's a breakdown of the various references I beleive should be studied in Microsoft Documentation.
Good things I think would be good to know
- Event-driven models of streaming data into storage and analytics
- Using kafka, IoT Hub, ...
- Configuring a ML Studio model
- Exposing an ML model as a web service
- Create a bot that uses:
- Azure functions
- QnA
- Timer
- Integrate a bot with a web app
- Perform an OCR of a set of images
- Monitor twitter and find negative tweets / send alerts
- Capture data from kafka into HD Insight
- IoT Edge demos: blob storage, functions, stream analytics
- Run an AI solution as a container instance
- IoT samples
- Output data from stream analytics to other data sources: CosmosDB, blobs, Azure SQL