Mar 13
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🖨️ Get the complete AI-102 PDF (300 Questions + Detailed Explanations) here: 👉 https://certifyai.gumroad.com/l/bgiicf 👉 https://certifyai.gumroad.com/l/bgiicf 👉 https://certifyai.gumroad.com/l/bgiicf Get hands-on with the hottest part of the AI-102! This set focuses on Azure OpenAI Service, including GPT-4 deployment, prompt engineering (system vs. user messages), and the RAG (Retrieval-Augmented Generation) pattern. Learn how to ground models in your own data and fine-tune hyperparameters like temperature and top_p to pass the GenAI section with confidence. #AzureOpenAI #promptengineering #RAG #gpt4 #aipc01practice #generativeai TIMESTAMP 0:00 - Disclaimer 0:14 - Session Format 0:35 - What to Expect 0:44 - Exam + Domain Scope 0:55 - Q1 – Implement model responses restricted to internal documents 1:23 - Q2 – Deploy GPT-4 with guaranteed throughput and consistent latency 1:51 - Q3 – Ensure Chat Completion outputs valid JSON for downstream processing 2:19 - Q4 – Prompt design with example queries and responses 2:47 - Q5 – Prevent model from generating harmful content about self-harm 3:15 - Q6 – Reduce hallucinations when summarizing long documents 3:43 - Q7 – Convert text into embeddings for storage in a vector database 4:11 - Q8 – Handle token-per-minute limit to scale application traffic 4:39 - Q9 – Enable assistant to execute Python code for calculations and charts 5:07 - Q10 – Describe internal application functions for model usage 5:35 - Q11 – Mitigate prompt injection attacks in generative AI 6:03 - Q12 – Deploy a model for image generation 6:31 - Q13 – Penalize repeated words or phrases in Chat Completion 6:59 - Q14 – Require step-by-step reasoning before final output 7:27 - Q15 – Specify resource type when deploying via ARM template 7:55 - Q16 – Improve retrieval quality using keywords and vector similarity 8:23 - Q17 – Ensure AI models do not retain or learn from your data 8:51 - Q18 – Define the purpose of the system role in a chat completion 9:19 - Q19 – Identify model families eligible for fine-tuning 9:47 - Q20 – Investigate high numbers of content-filtered errors 10:15 - Q21 – Estimate token count before sending long documents to API 10:43 - Q22 – Explain why documents are chunked before creating embeddings 11:11 - Q23 – Include citations of source documents in model responses 11:39 - Q24 – Ensure generative AI applications are accessible for all users 12:10 - Q25 – Interpret the significance of a model’s context length 12:38 - Playlist + PDF Access