Total points 4
1.
Question 1
What determines the maturity of the MLOps process?

1 / 1 point

The accuracy of the deployed model.


The standard pattern for MLOps building.


The technical scenario for MLOps implementation.


> The level of automation of ML pipelines.

Correct
Great job! Fundamentally, the level of automation of the Data Collection, Modeling, Deployment, and Maintenance systems determines the maturity of the MLOps process.

2.
Question 2
True Or False: At the basic level of maturity, or level 0, tracking or logging are required to detect model performance degradation and other model behavioral drifts.

1 / 1 point

True


> False

Correct
Exactly! Level 0 is concerned only with deploying the trained model as a prediction service (for example, a microservice with a REST API). This manual, data-scientist-driven process doesn’t track predictions and might be sufficient when models are rarely changed or retrained.

3.
Question 3
What steps do you need to introduce into the ML pipeline to move towards MLOps maturity level one? (Select all that apply)

1 / 1 point

Automated Theorem Proving


> Automated Data Validation

Correct
That's right! Automated data validation is necessary to decide whether your model should be retrained or the execution of the pipeline must stop. This decision is automatically made only if the data is deemed valid.


Dependently Typed Programming


> Automated Model Validation

Correct
Well done! Model Validation makes sure that the new model performs better than the current one before promoting it to production. Also, Model Validation ensures that model performance is consistent on various segments of the data.

4.
Question 4
In case of an interruption, what key component of the pipeline allows you to resume execution seamlessly?

1 / 1 point

Models Registry


> Metadata Store


Feature Store


Trigger

Correct
Nailed it! The metadata store tracks each pipeline execution, so you can rely on pointers to the artifacts produced at each step, like the location of prepared data, validation anomalies, or computed statistics, to resume execution in case of an interruption.