Large Language Models (LLM) ve Text Processing
1 | Purpose & Industry Rationale
Large Language Models (LLM) and Text Processing certification provides a quantitative, globally verifiable credential for professionals who can transform raw text into rich representations, design advanced LLM solutions, and deploy them into production.
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Market Momentum The global Natural Language Processing (NLP) market, valued at USD 25.9 billion in 2023, is projected to reach USD 189.6 billion by 2030, reflecting a 32.5 % CAGR.
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Talent Surge LinkedIn’s Future of Work report shows that AI/ML job postings grew 38 % between 2020 and 2024, while positions requiring generative-AI and LLM skills expanded exponentially over the same period.
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Salary Premiums As of 2025, U.S. Large Language Models Engineer roles command average annual salaries of USD 180 000 – 240 000, with senior positions exceeding this range.
2 | Large Language Models (LLM) and Text Processing Exam Architecture
| Feature | Details |
|---|---|
| Questions | 50 multiple-choice (single correct) |
| Duration | 120 minutes (≈ 1 min per question) |
| Pass Mark | 70 % |
| Delivery | Online, remotely proctored, instant scoring |
| Credential | Web + QR verification |
| Retakes | Two free attempts within 12 months |
3 | Assessed Competency Domains
(each row contains a representative stem drawn from the uploaded item bank)
| Domain | Example Stem |
|---|---|
| Text Cleaning & Tokenization | “What is the principal difference between BPE and WordPiece?” |
| Vectorization & Embedding Spaces | “In which scenarios is cosine similarity preferred over Euclidean distance?” |
| Transformers & Attention Mechanisms | “Why is the d<sub>k</sub> scaling factor required in Scaled-Dot-Product Attention?” |
| LLM Architecture & Scaling Laws | “What is the practical implication of the Chinchilla scaling law?” |
| Prompt Design & Engineering | “How do zero-shot, few-shot, and chain-of-thought prompting differ?” |
| Fine-Tuning & Adaptation (FT, LoRA, PEFT) | “Why are LoRA layers favoured over full back-propagation in certain settings?” |
| RAG & Vector Databases | “Under what circumstances do dense and sparse retrievers achieve similar performance?” |
| Evaluation Metrics | “List advantages and drawbacks of BERTScore relative to ROUGE-L.” |
| Ethics, Safety & Hallucination | “Which vulnerability is uncovered by prompt-based jailbreak tests?” |
Questions target the Apply–Analyze levels of Bloom’s taxonomy; pilot testing produced Cronbach’s α = 0.83, indicating high reliability.
4 | Certificate Value Proposition
| Benefit | Explanation |
|---|---|
| Global Recognition | The badge verifies on LinkedIn / GitHub with a single click. |
| Career Velocity & Salary Lift | Certified LLM specialists are hired faster and negotiate top-band salaries relative to peers. |
| Bid & Proposal Strength | Documented expertise earns bonus points in RFP evaluations. |
| Continuous Learning Ecosystem | Successful candidates receive a 25 % OptiWisdom Data Academy discount plus monthly live code-review sessions. |
5 | Scientific Design & Validity
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Item Response Theory was applied to remove low-discrimination items.
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Differential Item Functioning audits minimized cultural / linguistic bias.
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Technical Currency All scenarios align with Python 3.12, Hugging Face Transformers 0.22, FAISS 1.8, and LangChain 0.2 (Q2 2025).
Değerlendirmeler
Henüz değerlendirme yapılmadı.