A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?

• A. Using ML Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
• B. Using Databricks Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
• C. Using ML Runtime, use a moderate sized GPU VM for the driver and a variable number of memory optimized CPU VMs for the workers with autoscale enabled
• D. Using ML Runtime, use a large single node, GPU-enabled VM with auto termination set to 20 minutes to reduce costs

A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

• A. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.
• B. Use custom init scripts that install the necessary packages as part of the job.
• C. Use Databricks Repos to store the model code and install the required libraries in the notebook.
• D. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.

A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

• A. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
• B. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
  The model can then be scheduled as a nightly batch job that runs on a multi-node compute.
• C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
• D. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

• A. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
• B. The context parameter allows the user to document the performance of a model after it has been deployed
• C. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
• D. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
• E. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario

A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input. Which of the following MLflow operations can be used to perform this task?

• A. mlflow.models.signature.infer_signature
• B. mlflow.models.Model.signature
• C. mlflow.models.schema.infer_schema
• D. mlflow.models.Model.get_input_schema
• E. There is no way to obtain the input schema and the output schema of an unlogged model.