Quiz Time: The Foundations

Let's check what you have learned. In this quiz, there are six questions about the foundations of M L Ops. For each one, I will read the question, and four answers. Then you will have a few seconds to think, before I give the answer. Ready? Let's go.

• Six quick questions
• Pause, think, then check
• No pressure, just practice

Why MLOps

Question 1. Why do models silently fail in production? A, The code crashes. B, The data and the world change. C, The servers overheat. D, The model forgets its weights. Pause the video, and think. The answer is B. Data drifts away from the training data, so accuracy decays, without any error message.

Versioning

Question 2. What does D V C store in Git? A, The full dataset. B, A small pointer file with a fingerprint. C, The trained model weights. D, The server logs. Pause the video, and think. The answer is B. The big data lives in storage, and Git keeps a tiny pointer, linking each commit to its data.

Reproducibility

Question 3. Which four things must be fixed to reproduce a run? A, Seeds, data, code, environment. B, Speed, size, cost, colour. C, Model, label, metric, plot. D, Cloud, region, disk, memory. Pause the video, and think. The answer is A. Randomness, data, code, and libraries all influence the result, so all four must be pinned.

Leakage

Question 4. What is data leakage? A, Losing data on disk. B, Using information that would not exist at prediction time. C, Sharing data publicly. D, Deleting rows by mistake. Pause the video, and think. The answer is B. A leaky model looks brilliant in testing, and fails live, because the future information is missing.

Gates

Question 5. What does a registry quality gate do? A, Speeds up training. B, Promotes a model only if it beats the champion. C, Deletes old data. D, Encrypts the model. Pause the video, and think. The answer is B. The gate compares the candidate with the current champion, and rejects worse models automatically.

Metrics

Question 6. Why is accuracy misleading when only one customer in eight leaves? A, It is slow to compute. B, A model that always says stay scores high. C, It cannot be negative. D, It ignores the labels. Pause the video, and think. The answer is B. On imbalanced data, a useless model can score high accuracy, so use precision, recall, and F one.

Summary