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