Скачать или смотреть AIF-C01 Practice Test: Applications of Foundation Models - 28% Domain | Set 2 of 3

MASTER the biggest domain on the AIF-C01 exam!

This is Set 2 of 3 of our essential practice questions for Domain 3: Applications of Foundation Models, which accounts for a substantial 28% of the AWS Certified AI Practitioner exam.

This set challenges your ability to select the right Foundation Model (FM) for a business need and understand key concepts like Amazon Bedrock, RAG (Retrieval Augmented Generation), and Prompt Engineering.

These are exam-style questions designed to identify and close your knowledge gaps quickly.

🎯 Time yourself and see if you can handle the most heavily weighted questions!

🔔 Subscribe for Sets 2 & 3 to complete your Domain 3 review!

#AIFC01PracticeTest #AmazonBedrock #FoundationModels #RAG #PromptEngineering #AWSCertifiedAIPractitioner

TIMESTAMP
0:00 - Disclaimer
0:14 - Session Format
0:35 - What to Expect
0:44 - Exam + Domain Scope

0:58 - Q26 - Guardrail Action When User Prompt Violates a Defined Denial Policy
1:26 - Q27 - Primary Prerequisite for Successful Fine-Tuning Customization
1:54 - Q28 - Foundation Model Application Utilized to Distill Key Takeaways from Long Reports
2:22 - Q29 - Most Cost-Effective SageMaker Inference Type for High-Throughput Batch Processing
2:50 - Q30 - Most Effective Technique for Enforcing Consistent, Predictable JSON Output Format
3:18 - Q31 - Model Type that Converts Text into Semantic Numerical Representations for RAG
3:46 - Q32 - Responsible AI Principle Addressing the Need to Understand Model Recommendations
4:14 - Q33 - Most Crucial Model Characteristic for Analyzing and Comparing Long Documents
4:42 - Q34 - Model Compression Technique for Reducing Memory Footprint on Edge Devices
5:10 - Q35 - How Foundation Model is Updated with New Documents in a Bedrock Knowledge Base
5:38 - Q36 - Amazon-Developed Model Family for Cross-Modal Text and Image Embedding
6:06 - Q37 - Security Attack Involving Malicious Prompt Command Overriding Hidden Instructions
6:34 - Q38 - Foundation Model Application for Rewriting Formal Text into Conversational Tone
7:02 - Q39 - Technique Required to Deploy a Foundation Model Too Large for a Single GPU
7:30 - Q40 - Primary Consideration for Determining Optimal Text Chunk Size for RAG
7:58 - Q41 - Guardrails Element Used to Detect and Filter Hate Speech and Explicit Content
8:26 - Q42 - Preferred Technique for Highest Factual Accuracy on Static, Internal Data
8:54 - Q43 - Agent Orchestrator Response to a User Request Requiring Multiple Tool Calls
9:22 - Q44 - Prompting Technique Instructing Model to Review and Correct Own Output
9:50 - Q45 - Commercial Option for Guaranteed Minimum Performance and Predictable Throughput on Bedrock
10:18 - Q46 - Practical Example of Foundation Model Used for Social Media Comment Labeling
10:46 - Q47 - Aspect of Responsible AI Addressing the Tracking of Model Training Data Origin
11:14 - Q48 - Advanced Multimodal Application of AI Generating Code from a User Interface Screenshot
11:42 - Q49 - Primary Benefit of RAG Over Fine-Tuning When Knowledge Base is Extremely Large
12:10 - Q50 - Deployment Strategy for Testing New Foundation Model with Small Live Traffic Portion

12:38 - Playlist + PDF Access