AWS AIP-C01: Observability for Generative AI Applications
Generative AI observability must explain more than whether an API returned HTTP 200. Production teams need visibility into model choice, latency, token usage,…
Read articleVENDOR / EXAMTOPICS EDITORIAL
Amazon.
Amazon is the issuing vendor for the AWS certification program.
PUBLISHED COVERAGECurated by subject
AWS AIP-C01: Evaluating Foundation Models Pre-Deployment
Choosing a foundation model for production should be treated as an engineering decision, not a leaderboard decision. A model that scores well on…
Read articleAWS AIP-C01: Guardrails and Content Safety in Amazon Bedrock
Amazon Bedrock Guardrails provides configurable safeguards that can evaluate user input and model output against policies such as content filters, denied topics, sensitive-information…
Read articleAWS AIP-C01: Amazon Bedrock Cost Optimization
Amazon Bedrock cost depends on more than which foundation model an application invokes. Input tokens, output tokens, prompt caching, inference routing, provisioned capacity,…
Read articleAWS AIP-C01: Agentic AI Patterns with Amazon Bedrock
Agentic AI applications differ from ordinary prompt-response systems because the model can plan, call tools, retrieve context, preserve memory, and continue through several…
Read articleAWS DEA-C01: Kinesis Data Streams vs Firehose
Amazon Kinesis Data Streams and Amazon Data Firehose solve different streaming problems. Kinesis Data Streams is a durable real-time event stream for applications…
Read articleAWS AIP-C01: Building RAG Applications with Amazon Bedrock
Retrieval-Augmented Generation combines a foundation model with external evidence so responses can use enterprise documents, product knowledge, policies, support content, or other domain…
Read articleAWS DEA-C01: Lake Formation Permissions and Data Governance
AWS Lake Formation provides centralized governance for data lakes by adding fine-grained permissions over data stored in Amazon S3 and metadata in the…
Read articleAWS AIP-C01: CI/CD for Generative AI Applications
Generative AI applications need CI/CD, but the release artifact is broader than ordinary application code. A production change can involve prompt templates, model…
Read articleAWS DEA-C01: Redshift Distribution Styles and Sort Keys
Amazon Redshift performance depends heavily on how data is distributed across compute slices and how rows are ordered on storage. Distribution style affects…
Read articleAWS DEA-C01: Data Quality and Lineage in AWS Pipelines
Data pipelines can complete successfully while producing incorrect, stale, duplicated, or semantically broken output. That is why production data engineering needs two complementary…
Read articleAWS DEA-C01: EMR vs Glue for Large-Scale Data Processing
Amazon EMR and AWS Glue can both run distributed data processing on AWS, but they optimize for different levels of control. EMR gives…
Read articleAWS DEA-C01: Building Data Lakes on Amazon S3
Amazon S3 is a common foundation for AWS data lakes because it provides durable object storage, virtually unlimited scale, flexible prefixes, lifecycle controls,…
Read articleAWS DEA-C01: Athena Performance and Partitioning Strategy
Amazon Athena queries data directly in Amazon S3, so performance depends heavily on how that data is organized before SQL runs. Partitioning, columnar…
Read articleAWS DEA-C01: Glue Jobs, Crawlers & Data Catalog
AWS Glue combines metadata management and serverless data integration so teams can discover datasets, define schemas, transform data, and make results available to…
Read article