ExamPark
Course menuAIF-C01

Exams / AIF-C01 / ML lifecycle, MLOps and metrics / Question 1

AIF-C01 practice question: ML lifecycle, MLOps and metrics (1 of 3)

Fundamentals of AI and ML A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

Last verified against current AWS documentation ·How we write our questions

A bank is building its first ML model to flag suspicious card transactions.

Put these pipeline stages in the order they normally happen, first stage first.

Put these 5 steps in order.

  1. A. Train the model on the prepared training data.
  2. B. Collect historical transactions and their confirmed fraud outcomes.
  3. C. Deploy the model and monitor its predictions in production.
  4. D. Clean the data and engineer useful features.
  5. E. Evaluate the model against held-back test data.
Show the answer and explanation

Correct order: B, D, A, E, C

  1. B. Collect historical transactions and their confirmed fraud outcomes. First: the team needs past transactions with known results, because a model can only learn from examples that already have answers.
  2. D. Clean the data and engineer useful features. Second: raw transaction records contain gaps and errors, so they are cleaned and turned into useful inputs such as spending patterns.
  3. A. Train the model on the prepared training data. Third: training uses the cleaned, feature-rich data prepared in the previous stage to learn the patterns that separate fraud from genuine payments.
  4. E. Evaluate the model against held-back test data. Fourth: the model is scored on data it never trained on, which shows whether it generalizes before it is released.
  5. C. Deploy the model and monitor its predictions in production. Fifth: only after testing does the model go live, and it is monitored so that drops in accuracy are caught early.

Explanation

An ML pipeline starts with collecting labeled historical data, then prepares the data, trains the model, evaluates it on held-back data, and finally deploys and monitors it. Evaluating on data the model never saw is what prevents a model that only memorized its training set from going live. This question tests the normal order of the lifecycle.

Study

Keep practicing

Answer questions with a score at the end, or practice just this topic with the explanation after each one. No sign-up needed.

Take the free AIF-C01 practice examPractice ML lifecycle, MLOps and metricsAll ML lifecycle, MLOps and metrics questions

Want more on ML lifecycle, MLOps and metrics? The 5 full-length practice sets have 18 more ML lifecycle, MLOps and metrics questions, 325 in all, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets