Question 1

Which of the following statements describes streaming with Spark as a model deployment strategy?

Options :

A : The inference of batch processed records as soon as a trigger is hit

B : The inference of all types of records in real-time

C : The inference of batch processed records as soon as a Spark job is run

D : The inference of incrementally processed records as soon as trigger is hit

E : The inference of incrementally processed records as soon as a Spark job is run

Answer: E

Question 2

Which of the following is a benefit of logging a model signature with an MLflow model?

Options :

A : The model will have a unique identifier in the MLflow experiment

B : The schema of input data can be validated when serving models

C : The model can be deployed using real-time serving tools

D : The model will be secured by the user that developed it

E : The schema of input data will be converted to match the signature

Answer: E

Question 3

A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.

Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

Options :

A : The pyfunc model can be used to deploy models in a parallelizable fashion

B : The same preprocessing logic will automatically be applied when calling fit

C : The same preprocessing logic will automatically be applied when calling predict

D : This approach has no impact when loading the logged pyfunc model for downstream deployment

E : There is no longer a need for pipeline-like machine learning objects

Answer: E

Question 4

A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:

1. Deploy a model to production and compute predicted values

2. Obtain the observed (actual) label values

3. _____

4. Run a statistical test to determine if there are changes over time

Which of the following should be completed as Step #3?

Options :

A : Obtain the observed values (actual) feature values

B : Measure the latency of the prediction time

C : Retrain the model

D : None of these should be completed as Step #3

E : Compute the evaluation metric using the observed and predicted values

Answer: D

Question 5

A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.

They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

Options :

A : log_artifact

B : log_model

C : log_metric

D : log_param

E : There is no way to store values like this.

Answer: A

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