AAIA Course #9: Data Management Specific to AI

Learn how to trace AI decisions back to their data and catch records silently excluded before the model ever sees them. This video covers the data controls auditors evaluate: pipelines, labeling quality, feature stores, and the critical distinction between data drift and concept drift — the distinction exam candidates miss most often. Data management is where AI audits either find the root cause or miss it entirely. Bad data upstream guarantees bad decisions downstream. You'll master source-to-score reconciliation, understand why a pipeline can fail "successfully" with no output to inspect, and learn the three dimensions for evaluating data inputs: appropriateness, bias, and privacy. Key Topics: • AI data pipelines: ingestion, labeling quality, and the risks of mislabeled or incomplete training data • Feature stores, dataset versioning, and reproducibility of training inputs • Data drift versus concept drift — what changes and why each matters for auditors • Source-to-score reconciliation to detect silently excluded records • Five controls auditors look for in data management Chapters: 0:00 Introduction 0:43 Previously On 1:16 Key Terms 2:03 Why Data Comes First 2:56 The AI Data Pipeline 5:49 Labeling Quality and Mislabeled Data 8:15 Feature Stores and Training-Serving Skew 11:39 Data Drift vs. Concept Drift 16:18 Evaluating Data Inputs: Three Dimensions 18:46 Source-to-Score Reconciliation 22:49 Five Controls to Look For 23:43 Practice Questions 🔗 ISACA AAIA Exam: https://www.isaca.org/credentialing/aaia 📋 Full Playlist: https://www.youtube.com/@StackLessons... StackLessons creates hands-on exam prep content for cloud & AI certifications. Like & Subscribe for more content! Need more practice questions? Visit https://certcompanion.com/exams #ISACA #AAIA #DataManagement 📌 Chapters 0:00 Introduction 0:43 Previously On 1:16 Key Terms 2:03 Why Data Comes First 2:56 The AI Data Pipeline 5:49 Labeling Quality and Mislabeled Data 8:15 Feature Stores and Training-Serving Skew 11:39 Data Drift vs. Concept Drift 16:18 Evaluating Data Inputs: Three Dimensions 18:46 Source-to-Score Reconciliation 22:49 Five Controls to Look For 23:43 Practice Questions #ISACA #AAIA #DataManagement