A company has multiple data scientists working on different ML projects. They want to ensure that experiments are reproducible and that models can be retrained consistently. Which MLOps practice should they adopt?

Use version control for code and data, and track experiments with metadata

Retrain models only when the original data scientist leaves

Use a single shared model for all projects

Manually record experiments in a shared spreadsheet

AWSCertified AI Practitioner

Domain 1Objective 3

Task Statement 1.3: Describe the AI/ML Development Lifecycle. AIF-C01 Practice Questions (Page 6)

Part of the Content Domain 1: Fundamentals of AI and ML domain, which makes up ~20% of our current practice bank. AWS does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–12 in this domain), expect 2–4 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

7concepts

Questions 26–29

26foundation · easy
In an AI/ML pipeline, which stage immediately follows model training and precedes model deployment?
Select an answer first
27foundation · easy
Which MLOps concept involves tracking experiments, recording parameters, metrics, and artifacts to ensure that ML experiments are reproducible and comparable?
Select an answer first
28foundation · easy
Which model deployment method involves using a fully managed service that provides an API endpoint for inference, without the need to manage the underlying infrastructure?
Select an answer first
29application · medium
A company has multiple data scientists working on different ML projects. They want to ensure that experiments are reproducible and that models can be retrained consistently. Which MLOps practice should they adopt?
Select an answer first

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Task Statement 1.3: Describe the AI/ML Development Lifecycle. AIF-C01 Practice Questions (Page 6)

Questions 26–29

In an AI/ML pipeline, which stage immediately follows model training and precedes model deployment?

Which MLOps concept involves tracking experiments, recording parameters, metrics, and artifacts to ensure that ML experiments are reproducible and comparable?

Which model deployment method involves using a fully managed service that provides an API endpoint for inference, without the need to manage the underlying infrastructure?

A company has multiple data scientists working on different ML projects. They want to ensure that experiments are reproducible and that models can be retrained consistently. Which MLOps practice should they adopt?

Keep studying — Content Domain 1: Fundamentals of AI and ML