AWS AIP-C01 Practice Exam: Implementation & Integration (Domain 2) - Part 6 of 20
Автор: CertifyAI
Загружено: 2025-12-28
Просмотров: 16
This AIP-C01 practice exam focuses on Domain 2, testing your understanding of how GenAI solutions are implemented and integrated in AWS environments.
Practice exam topics:
• GenAI application architecture
• API and service integrations
• RAG pipelines and orchestration
• Deployment and integration scenarios
Designed to simulate real AWS AIP-C01 exam questions.
#AWSAIP #AWSAIPC01 #AIImplementation #GenAIIntegration #CloudAI #AWSExamPrep #AIPC01Practice #AIArchitecture #AIPractitioner #ArtificialIntelligence #AWSCertification #RAGPipeline
TIMESTAMP
0:00 - Disclaimer
0:14 - Session Format
0:35 - What to Expect
0:44 - Exam + Domain Scope
0:55 - Q26-Bedrock KB Embedding Model Semantic Search Role
1:23 - Q27-Code-Optimized Bedrock Model for Dev AI Assist
1:51 - Q28-Bedrock Converse API Multi-Model Simplified Chat
2:19 - Q29-GenAI Grounding for Verified Response Accuracy
2:47 - Q30-Bedrock LangChain Connector Class Integration
3:15 - Q31-Batch Summarization Cost-Efficient Bedrock Feature
3:43 - Q32-RAG Metadata Filtering for Secure Context Access
4:11 - Q33-Bedrock Header for AI Usage Attribution Tracking
4:39 - Q34-Fine-Tuned Bedrock Model API Access Path
5:07 - Q35-Context Window Limit for LLM Integration Design
5:35 - Q36-Bedrock Agent Session State Storage on AWS
6:03 - Q37-Bedrock Guardrails Content Filter Safety Layer
6:31 - Q38-Lambda Timeout Impact on Bedrock Response Processing
6:59 - Q39-Bedrock Cross-Region Inference Availability
7:27 - Q40-Official AWS SDK for Python Bedrock Integration
7:55 - Q41-Parent Document Retrieval for Better RAG Answers
8:23 - Q42-Purchase History Injection for GenAI Personalization
8:51 - Q43-Bedrock Model Evaluation UI for No-Code Testing
9:19 - Q44-Stop Sequences for Controlled LLM Output Boundaries
9:47 - Q45-Consistent Technical Term Translation in Bedrock
10:15 - Q46-Top P Sampling Control for AI Output Diversity
10:43 - Q47-Secure Bedrock API Calls from React Frontend
11:11 - Q48-Aurora Vector Store Extension for RAG Integration
11:39 - Q49-Bedrock Agent Lambda Call Success Monitoring
12:10 - Q50-Bedrock vs SageMaker AI Model Hosting Difference
12:38 - Playlist + PDF Access
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