https://cs.jdoner.me/podcasts/its-ai/ en-us Lessons on the Certiport IT Specialist Artificial Intelligence exam objectives, one domain per episode, with a bonus episode of practice questions after each lesson. IT Specialist AI exam prep false edition-its-ai-d6p-practice-monitoring-in-production Fri, 11 Sep 2026 18:52:17 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:18:47 full edition-its-ai-d6-monitoring-in-production Fri, 11 Sep 2026 18:49:22 -0700 1. Objective 6.1, keeping the running system under oversight Watch: NIST AI Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework Watch: OWASP Top 10 for LLM Applications, https://genai.owasp.org/llm-top-10/ Watch: Google's Machine Learning Crash Course, including production ML systems, https://developers.google.com/machine-learning/crash-course 2. Objective 6.2, measuring the business impact Watch: NIST AI Risk Management Framework Playbook, https://airc.nist.gov/airmf-resources/playbook/ 3. Objective 6.3, measuring the impact on people and communities Watch: NIST AI Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework Watch: MLU-Explain, visual essays including equality of odds, https://mlu-explain.github.io/ 4. Objective 6.4, handling what users tell you Watch: NIST AI Risk Management Framework Playbook, https://airc.nist.gov/airmf-resources/playbook/ 5. Objective 6.5, reviewing the whole operation on a schedule Watch: NIST AI Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework Watch: NIST AI Risk Management Framework 1.0, publication AI 100-1, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf 6. Review and takeaways 0:22:21 full edition-its-ai-d5p-practice-integrity-and-deployment Fri, 11 Sep 2026 18:46:42 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:17:54 full edition-its-ai-d5-integrity-and-deployment Fri, 11 Sep 2026 18:44:12 -0700 1. Objective 5.1, the production pipeline and the application Watch: Google's Machine Learning Crash Course, including production ML systems, https://developers.google.com/machine-learning/crash-course Watch: Practical Deep Learning for Coders, the free fast dot ai course, https://course.fast.ai/ 2. Objective 5.2, planning for what goes wrong in production Watch: OWASP Top 10 for LLM Applications, https://genai.owasp.org/llm-top-10/ Watch: Google's Machine Learning Crash Course, including production ML systems, https://developers.google.com/machine-learning/crash-course 3. Objective 5.3, building the security plan Watch: OWASP Top 10 for LLM Applications, https://genai.owasp.org/llm-top-10/ Watch: MITRE ATLAS, the adversarial threat matrix for AI systems, https://atlas.mitre.org/ Watch: NIST Adversarial Machine Learning taxonomy, AI 100-2, https://csrc.nist.gov/pubs/ai/100/2/e2025/final 4. Objective 5.4, teaching customers what the product does and does not do Watch: NIST AI Risk Management Framework Playbook, https://airc.nist.gov/airmf-resources/playbook/ Watch: NIST AI Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework 5. Review and takeaways 0:22:43 full edition-its-ai-d4bp-practice-bias-selection-and-results Fri, 11 Sep 2026 18:43:13 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:18:45 full edition-its-ai-d4b-bias-selection-and-results Fri, 11 Sep 2026 18:41:03 -0700 1. Objective 4.4, finding where bias could enter Watch: Google's Machine Learning Crash Course, including the fairness module, https://developers.google.com/machine-learning/crash-course Watch: MLU-Explain, visual essays including equality of odds, https://mlu-explain.github.io/ Watch: NIST AI Risk Management Framework, https://www.nist.gov/itl/ai-risk-management-framework 2. Objective 4.5, selecting one model after experimenting Watch: Google's Machine Learning Crash Course, https://developers.google.com/machine-learning/crash-course Watch: Dive into Deep Learning, the free textbook, https://d2l.ai/ Watch: MIT OpenCourseWare 6.036, Introduction to Machine Learning, https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/ 3. Objective 4.6, presenting the result and getting approval Watch: NIST AI Risk Management Framework Playbook, https://airc.nist.gov/airmf-resources/playbook/ Watch: MLU-Explain, visual essays on model evaluation, https://mlu-explain.github.io/ 4. Review and takeaways 0:18:52 full edition-its-ai-d4ap-practice-choosing-and-training-models Fri, 11 Sep 2026 18:40:08 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:19:23 full edition-its-ai-d4a-choosing-and-training-models Fri, 11 Sep 2026 18:40:07 -0700 1. Objective 4.1, the algorithm families and picking candidates Watch: Google's Machine Learning Crash Course, https://developers.google.com/machine-learning/crash-course Watch: Elements of AI, the free introduction from the University of Helsinki, https://www.elementsofai.com/ Watch: Hugging Face LLM Course, chapter one, https://huggingface.co/learn/llm-course/chapter1/1 2. Objective 4.2, training a model and changing the settings Watch: Google's Machine Learning Crash Course, https://developers.google.com/machine-learning/crash-course Watch: CS50's Introduction to AI with Python, https://cs50.harvard.edu/ai/ Watch: Practical Deep Learning for Coders, the free fast dot ai course, https://course.fast.ai/ 3. Objective 4.3, evaluating accuracy, precision, and sensitivity Watch: MLU-Explain, visual essays on the confusion matrix, precision and recall, https://mlu-explain.github.io/ Watch: Google's Machine Learning Crash Course, https://developers.google.com/machine-learning/crash-course Watch: Dive into Deep Learning, the free textbook, https://d2l.ai/ 4. Review and takeaways 0:21:07 full edition-its-ai-d3p-practice-feature-engineering Fri, 11 Sep 2026 18:40:05 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:19:06 full edition-its-ai-d3-feature-engineering Fri, 11 Sep 2026 17:39:44 -0700 1. Selecting the features Watch: Google Machine Learning Crash Course, feature engineering and data preparation, https://developers.google.com/machine-learning/crash-course Watch: MIT OpenCourseWare 6.036, introduction to machine learning, features and representation, https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/ 2. Transforming the features Watch: Google Machine Learning Crash Course, numerical data and transformations, https://developers.google.com/machine-learning/crash-course Watch: MIT OpenCourseWare 6.036, feature transformation and scaling, https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/ 3. Splitting into training and test sets Watch: Google Machine Learning Crash Course, datasets, generalization, and overfitting, https://developers.google.com/machine-learning/crash-course Watch: MIT OpenCourseWare 6.036, evaluation and held out data, https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/ 4. Writing the decisions down Watch: NIST AI Risk Management Framework, Govern, Map, Measure, and Manage, https://www.nist.gov/itl/ai-risk-management-framework Watch: NIST AI RMF Playbook, documentation and transparency actions, https://airc.nist.gov/airmf-resources/playbook/ 5. Review and takeaways 0:20:46 full edition-its-ai-d2p-practice-data-collection-and-transformation Fri, 11 Sep 2026 18:37:19 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:18:44 full edition-its-ai-d2-data-collection-and-transformation Fri, 11 Sep 2026 18:34:50 -0700 1. Where the data comes from Watch: Google Machine Learning Crash Course, datasets and data characteristics, https://developers.google.com/machine-learning/crash-course Watch: NIST AI RMF Playbook, mapping data provenance, https://airc.nist.gov/airmf-resources/playbook/ 2. Judging whether the data is good enough Watch: Google Machine Learning Crash Course, fairness and data bias, https://developers.google.com/machine-learning/crash-course Watch: MLU-Explain, a visual walk through class imbalance and equality of odds, https://mlu-explain.github.io/ 3. Putting the data into a form a model can use Watch: Google Machine Learning Crash Course, working with numerical and categorical data, https://developers.google.com/machine-learning/crash-course Watch: Hugging Face LLM Course, chapter one, how text becomes tokens, https://huggingface.co/learn/llm-course/chapter1/1 4. Review and takeaways 0:17:54 full edition-its-ai-d1bp-practice-problem-definition Fri, 11 Sep 2026 18:33:44 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:19:17 full edition-its-ai-d1b-problem-definition Fri, 11 Sep 2026 18:30:54 -0700 1. Planning for responsible use Watch: NIST AI Risk Management Framework, the four functions, https://www.nist.gov/itl/ai-risk-management-framework Watch: NIST AI RMF Playbook, suggested actions for Map and Measure, https://airc.nist.gov/airmf-resources/playbook/ 2. What you disclose and what you validate Watch: NIST AI Risk Management Framework 1.0, transparency and documentation, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf Watch: NIST AI RMF Playbook, the Govern function, https://airc.nist.gov/airmf-resources/playbook/ 3. Review and takeaways 0:13:34 full edition-its-ai-d1ap-practice-problem-definition Fri, 11 Sep 2026 18:30:52 -0700 1. Question 1 2. Question 2 3. Question 3 4. Question 4 5. Question 5 6. Question 6 7. Question 7 8. Question 8 9. Question 9 10. Question 10 0:18:27 full edition-its-ai-d1a-problem-definition Fri, 11 Sep 2026 18:28:16 -0700 1. Stating the problem Watch: Elements of AI, chapter one, what AI is and is not, https://www.elementsofai.com/ Watch: NIST AI Risk Management Framework, the Map function, https://www.nist.gov/itl/ai-risk-management-framework 2. The expertise and resources the problem needs Watch: NIST AI RMF Playbook, Govern and Map, roles and resources, https://airc.nist.gov/airmf-resources/playbook/ Watch: Google Machine Learning Crash Course, production ML systems, https://developers.google.com/machine-learning/crash-course 3. Classifying the problem by learning type Watch: Google Machine Learning Crash Course, linear and logistic regression, https://developers.google.com/machine-learning/crash-course Watch: CS50's Introduction to AI with Python, the learning lecture, https://cs50.harvard.edu/ai/ Watch: Elements of AI, chapter four, machine learning, https://www.elementsofai.com/ 4. Review and takeaways 0:17:13 full