AI World 1 · GenAI Foundations · Stage 2

Pick the Model

Modality, context window, latency, where the weights live, and how each call is paid for.

Model choice
Context window
Batch inference
Provisioned Throughput
Latency-optimized inference
Custom Model Import

Domain: AI and machine learning
World: AI World 1: GenAI Foundations (Associate)
Game type: Solution board: Drop services into each row's slots, then run records through them.
Time: About 25 minutes on a first attempt (estimated)
Exam guides: AIF-C01Task 1.3: Describe the AI/ML development lifecycle; Task 2.1: Explain the basic concepts of generative AI (GenAI); Task 2.2: Understand the capabilities and limitations of GenAI for solving business problems; Task 3.1: Describe design considerations for applications that use foundation models (FMs)
MLA-C02Skill 2.1.1: Evaluate and select appropriate FMs from Amazon Bedrock based on task requirements and performance criteria; Skill 2.1.7: Assess tradeoffs between AI model performance, latency, and cost; Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options; Skill 3.1.5: Deploy models that were built outside of AWS into AWS environments (for example, Amazon SageMaker AI, Amazon Bedrock Custom Model Import); Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency; Skill 4.2.7: Evaluate cost implications of using FMs for inference in production
AIP-C01Skill 1.2.1: Assess and choose FMs to ensure optimal alignment with specific business use cases and technical requirements (for example, by using performance benchmarks, capability analysis, limitation evaluation); Skill 2.2.1: Deploy FMs based on specific application needs and performance requirements (for example, by using Lambda functions for on-demand invocation, Amazon Bedrock provisioned throughput configurations, SageMaker AI endpoints to implement hybrid solutions); Skill 2.2.3: Develop optimized FM deployment approaches to balance performance and resource requirements for GenAI workloads (for example, by selecting appropriate models, by using smaller pre-trained models for specific tasks, by using API-based model cascading to perform routine queries); Skill 4.1.2: Create cost-effective model selection frameworks (for example, by using cost-capability tradeoff evaluation, tiered FM usage based on query complexity, inference cost balancing against response quality, price-to-performance ratio measurement, efficient inference patterns); Skill 4.2.1: Create responsive AI systems to address latency-cost tradeoffs and improve the user experience with FMs (for example, by using pre-computation to perform predictable queries, latency-optimized Amazon Bedrock models for time-sensitive applications, parallel requests for complex workflows, response streaming, performance benchmarking); Skill 4.2.3: Implement FM throughput optimization to address the specific throughput challenges of GenAI workloads (for example, by using token processing optimization, batch inference strategies, concurrent model invocation management)