Objective Coverage

Every published NCA-GENL exam objective, matched against the modules that teach it — 30 of 31 objectives have at least one module claiming them today.

1. 1.1Assist in deployment and evaluation of model scalability, performance, and reliability under supervision of senior team members.

   Covered by M11 · Fine-tuning, LoRA, and RLHF, M12 · Model deployment, serving, and optimization — 23 of 23 lessons authored.

2. 1.2Awareness of extracting insights from large datasets using data mining, data visualization, and similar techniques.

   Covered by M08 · Data analysis, curation, and visualization — 5 of 5 lessons authored.

3. 1.3Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.

   Covered by M04 · Transformer architecture and text generation, M05 · Prompt engineering, M06 · Document ingestion and chunking for RAG, M07 · Retrieval-augmented generation (RAG), M09 · Model evaluation metrics and methods, M11 · Fine-tuning, LoRA, and RLHF — 50 of 50 lessons authored.

4. 1.4Curate and embed content datasets for RAGs.

   Covered by M03 · Embeddings and vector representations, M05 · Prompt engineering, M06 · Document ingestion and chunking for RAG, M07 · Retrieval-augmented generation (RAG), M08 · Data analysis, curation, and visualization, M11 · Fine-tuning, LoRA, and RLHF — 41 of 41 lessons authored.

5. 1.5Familiarity with fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).

   Covered by M01 · LLM foundations and evaluation basics, M02 · Tokenization and text preprocessing, M04 · Transformer architecture and text generation, M09 · Model evaluation metrics and methods, M11 · Fine-tuning, LoRA, and RLHF, M12 · Model deployment, serving, and optimization — 56 of 56 lessons authored.

6. 1.6Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).

   Covered by M0 · Prerequisites and setup, M01 · LLM foundations and evaluation basics, M02 · Tokenization and text preprocessing, M03 · Embeddings and vector representations, M06 · Document ingestion and chunking for RAG, M07 · Retrieval-augmented generation (RAG) — 40 of 40 lessons authored.

7. 1.7Read research papers to identify emerging LLM trends and technologies.

   Covered by M01 · LLM foundations and evaluation basics, M04 · Transformer architecture and text generation, M10 · Experimentation: A/B testing and benchmarks, M11 · Fine-tuning, LoRA, and RLHF, M13 · Trustworthy AI: ethics, bias, and privacy — 36 of 36 lessons authored.

8. 1.8Select and use models to create text embeddings.

   Covered by M01 · LLM foundations and evaluation basics, M03 · Embeddings and vector representations, M07 · Retrieval-augmented generation (RAG), M09 · Model evaluation metrics and methods — 38 of 38 lessons authored.

9. 1.9Use prompt engineering principles to create prompts to achieve desired results.

   Covered by M04 · Transformer architecture and text generation, M05 · Prompt engineering, M07 · Retrieval-augmented generation (RAG), M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks, M11 · Fine-tuning, LoRA, and RLHF — 50 of 50 lessons authored.

10. 1.10Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.

    Covered by M0 · Prerequisites and setup, M01 · LLM foundations and evaluation basics, M02 · Tokenization and text preprocessing, M08 · Data analysis, curation, and visualization — 24 of 24 lessons authored.

11. 4.1Assist in the deployment and evaluations of model scalability, performance, and reliability under supervision of a senior team member.

    Covered by M11 · Fine-tuning, LoRA, and RLHF, M12 · Model deployment, serving, and optimization — 23 of 23 lessons authored.

12. 4.2Build LLM use cases such as RAGs, chatbots, and summarizers.

    Covered by M05 · Prompt engineering, M07 · Retrieval-augmented generation (RAG), M11 · Fine-tuning, LoRA, and RLHF — 27 of 27 lessons authored.

13. 4.3Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).

    Covered by M02 · Tokenization and text preprocessing, M03 · Embeddings and vector representations, M07 · Retrieval-augmented generation (RAG) — 23 of 23 lessons authored.

14. 4.4Identify system data, hardware, or software components required to meet user needs.

    Covered by M02 · Tokenization and text preprocessing, M04 · Transformer architecture and text generation, M06 · Document ingestion and chunking for RAG, M07 · Retrieval-augmented generation (RAG), M11 · Fine-tuning, LoRA, and RLHF, M12 · Model deployment, serving, and optimization, M13 · Trustworthy AI: ethics, bias, and privacy — 60 of 60 lessons authored.

15. 4.5Monitor functioning of data collection, experiments, and other software processes.

    Covered by M01 · LLM foundations and evaluation basics, M05 · Prompt engineering, M07 · Retrieval-augmented generation (RAG), M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks, M12 · Model deployment, serving, and optimization — 57 of 57 lessons authored.

16. 4.6Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.

    Covered by M0 · Prerequisites and setup, M01 · LLM foundations and evaluation basics, M02 · Tokenization and text preprocessing, M08 · Data analysis, curation, and visualization — 24 of 24 lessons authored.

17. 4.7Write software components or scripts under the supervision of a senior team member.

    Covered by M05 · Prompt engineering, M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks, M12 · Model deployment, serving, and optimization — 37 of 37 lessons authored.

18. 3.1Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

    Not yet claimed by any module — a known gap.

19. 3.2Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

    Covered by M01 · LLM foundations and evaluation basics, M09 · Model evaluation metrics and methods — 21 of 21 lessons authored.

20. 3.3Conduct data analysis under the supervision of a senior team member.

    Covered by M09 · Model evaluation metrics and methods — 13 of 13 lessons authored.

21. 3.4Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

    Covered by M08 · Data analysis, curation, and visualization — 5 of 5 lessons authored.

22. 3.5Identify relationships and trends or any factors that could affect the results of research.

    Covered by M08 · Data analysis, curation, and visualization, M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks, M12 · Model deployment, serving, and optimization, M13 · Trustworthy AI: ethics, bias, and privacy — 45 of 45 lessons authored.

23. 2.1Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

    Covered by M02 · Tokenization and text preprocessing, M06 · Document ingestion and chunking for RAG, M08 · Data analysis, curation, and visualization — 15 of 15 lessons authored.

24. 2.2Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

    Covered by M01 · LLM foundations and evaluation basics, M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks — 25 of 25 lessons authored.

25. 2.3Conduct data analysis under the supervision of a senior team member.

    Covered by M06 · Document ingestion and chunking for RAG, M08 · Data analysis, curation, and visualization, M09 · Model evaluation metrics and methods — 22 of 22 lessons authored.

26. 2.4Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

    Covered by M08 · Data analysis, curation, and visualization, M12 · Model deployment, serving, and optimization — 19 of 19 lessons authored.

27. 2.5Identify relationships and trends or any factors that could affect the results of research.

    Covered by M08 · Data analysis, curation, and visualization, M09 · Model evaluation metrics and methods, M10 · Experimentation: A/B testing and benchmarks, M12 · Model deployment, serving, and optimization, M13 · Trustworthy AI: ethics, bias, and privacy — 45 of 45 lessons authored.

28. 5.1Describe the ethical principles of trustworthy AI.

    Covered by M07 · Retrieval-augmented generation (RAG), M09 · Model evaluation metrics and methods, M13 · Trustworthy AI: ethics, bias, and privacy — 34 of 34 lessons authored.

29. 5.2Describe the balance between data privacy and the importance of data consent.

    Covered by M07 · Retrieval-augmented generation (RAG), M13 · Trustworthy AI: ethics, bias, and privacy — 21 of 21 lessons authored.

30. 5.3Describe how to use NVIDIA and other technologies to improve AI trustworthiness.

    Covered by M07 · Retrieval-augmented generation (RAG), M13 · Trustworthy AI: ethics, bias, and privacy — 21 of 21 lessons authored.

31. 5.4Describe how to minimize bias in AI systems.

    Covered by M08 · Data analysis, curation, and visualization, M09 · Model evaluation metrics and methods, M13 · Trustworthy AI: ethics, bias, and privacy — 27 of 27 lessons authored.

1 objective above (3.1) have no module claiming them yet.

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