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PMLEProfessional Machine Learning Engineer

Practice Exam 1

5 of this form’s 50 questions, drawn from across its domains, free. The explanation on every one of them is free too, at every tier, and always will be.

  1. Question 1 of 5

    1-1Architecting low-code AI solutions

    A build fails after an engineer places an aggregation over a group inside the preprocessing clause of a model definition. What does that clause forbid?

  2. Question 2 of 5

    2-3Collaborating within and across teams to manage data and models

    An engineer logs a validation loss at every training step and a final test accuracy once. Which metric kind holds the per step values?

  3. Question 3 of 5

    3-3Scaling prototypes into ML models

    A team trains a ranking model dominated by matrix computation with ultra-large embeddings, over a run lasting several weeks. Which hardware is indicated?

  4. Question 4 of 5

    4-2Serving and scaling models

    A team plans to run production serving on a nightly optimized runtime image to pick up the newest improvements. What should stop them?

  5. Question 5 of 5

    5-1Automating and orchestrating ML pipelines

    An engineer omits the job identifier when submitting a pipeline run and needs to locate that run afterwards. What identifier is used?

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The other 45 questions are the rest of this form: same 120 minute clock, same 750 cut score, and the same explanation on every question, which is never behind the wall. Practice Exam 1 of 2 on ML Engineer.

What this exam covers

  • Architecting low-code AI solutions7 questions / 13% of the exam
  • Collaborating within and across teams to manage data and models8 questions / 16% of the exam
  • Scaling prototypes into ML models10 questions / 21% of the exam
  • Serving and scaling models10 questions / 20% of the exam
  • Automating and orchestrating ML pipelines9 questions / 18% of the exam
  • Monitoring AI solutions6 questions / 13% of the exam

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