AWS AIP-C01 Practice Exam: Testing & Troubleshooting (Domain 5) - Part 17 of 20 скачать в хорошем качестве

AWS AIP-C01 Practice Exam: Testing & Troubleshooting (Domain 5) - Part 17 of 20

Validate your exam readiness with this AWS AIP-C01 practice exam focused on testing, validation, and troubleshooting GenAI systems. Practice exam covers: • Model and output evaluation • Prompt testing and regression testing • Diagnosing hallucinations and bias • Continuous monitoring and improvement Perfect for final review before exam day. #AWSAIP #AWSAIPC01 #AITesting #AIValidation #AITroubleshooting #GenAI #AWSExamPrep #AIPractitioner #AIPC01Practice #ArtificialIntelligence #CloudAI #ModelEvaluation #AIExamPrep TIMESTAMP 0:00 - Disclaimer 0:14 - Session Format 0:35 - What to Expect 0:44 - Exam + Domain Scope 0:55 - Q1-Metric Prioritization for Brand Tone Consistency in RAG Applications 1:23 - Q2-Validating Groundedness to Prevent LLM Hallucinations 1:51 - Q3-Grammatical Flow & Readability Metric for Text Summarization 2:19 - Q4-Troubleshooting Prompt Bloat Impact on Latency 2:47 - Q5-Multi-Lingual Model Failure Investigation 3:15 - Q6-A/B Testing with Traffic Splitting in AWS for Prompts 3:43 - Q7-Tuning Vector Search Relevance in RAG Pipelines 4:11 - Q8-Enforcing PII Guardrails in Bedrock 4:39 - Q9-Troubleshooting Bedrock InvokeModel Throttling 5:07 - Q10-Limitations of ROUGE for Semantically Correct Summaries 5:35 - Q11-Inspecting Retrieved Segments to Debug Knowledge Base Output 6:03 - Q12-Isolating Prompt Constraint Issues for Troubleshooting 6:31 - Q13-Purpose of Reference Dataset in Bedrock Model Evaluation 6:59 - Q14-Adjusting Max Tokens to Prevent Response Cut-Off 7:27 - Q15-Testing Model Robustness for Input Variations 7:55 - Q16-Bedrock Guardrail for Blocking Competitor Product Discussion 8:23 - Q17-Identifying Latency Bottlenecks in RAG Pipelines 8:51 - Q18-Using BERTScore over BLEU for High-Stakes Translation 9:19 - Q19-Common Cause of AccessDeniedException in Bedrock 9:47 - Q20-Relevance Metric for Knowledge Base Retrieval 10:15 - Q21-Architectural Fix for Consistently Toxic LLM Outputs 10:43 - Q22-LLM-as-a-Judge Workflow Orchestration in AWS 11:11 - Q23-Tuning Repetition Penalty for Looping Responses 11:39 - Q24-Troubleshooting Knowledge Base Empty Query Results 12:10 - Q25-Interpreting ROUGE-1 vs ROUGE-L Discrepancies 12:38 - Playlist + PDF Access