A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which of the following code blocks can they use to accomplish the task?

• A.
• B.
• C.
• D.
• E.

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A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

• A.The model signature must be provided, as Unity Catalog requires all models to have signatures.
• B.The cluster must be configured with shared access mode instead of dedicated mode.
• C.The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
• D.The model artifacts must be stored in DBFS rather than Unity Catalog volumes.

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A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

• A.This can only be viewed in the MLflow Experiments UI
• B.mlflow.sklearn.load_model(model_uri)
• C.client.list_artifacts(run_id)["feature-importances.csv"]
• D.mlflow.load_model(model_uri)
• E.client.pyfunc.load_model(model_uri)

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A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
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?

• A.mlflow.sklearn.load_model("model")
• B.mlflow.sklearn.log_model(sklearn_model, "model")
• C.mlflow.sklearn.track_model(sklearn_model, "model")
• D.mlflow.spark.log_model(sklearn_model, "model")
• E.mlflow.spark.track_model(sklearn_model, "model")

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A retail company wants to better forecast their sales of each SKU in every store in order to more accurately distribute their products. To achieve this, a Data Scientist proposes scaling their existing forecasting model to forecast individually for each combination of SKU and Store ID.
They have a cluster with 12 executors available in order to execute this. The current model is written using Pandas and the Prophet library for forecasting, with a Python function that receives a Pandas Data Frame with historic sales data as a parameter to train the forecasting model. For their next iteration, they want to improve the efficiency of this approach while using the least amount of effort and leveraging all available resources. Which approach will do this?

• A.1. Rewrite the function using the Generalized Linear Regression models available in Spark MLlib, which allows parameterizing to reflect a similar behavior to that of Prophet;
  2. Perform a GROUP BY on the Spark DataFrame with the training data to group by Store and SKU;
  3. Apply the Spark UDF to the grouped DataFrame with the apply function.
• B.1. Use a Spark query to identify all combinations of SKU and Store ID;
  2. Run a loop through every combination calling the existing Python function using Spark repartition(12) function so it distributes across all available executors.
• C.1. Decorate the existing training function with the @pandas_udf;
  2. On the Spark DataFrame with all training data perform a GROUP BY by Store and SKU;
  3. Apply the decorated function to the grouped DataFrame with the apply function.
• D.1. Use Spark repartition(12) to scale across all executors;
  2. Run to Pandas to convert the repartitioned DataFrame to Pandas so it can run on the original existing function;
  3. Add the Store ID and SKU to the Prophet model as exogenous variables.

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