Free Practice Questions for Databricks Databricks-Machine-Learning-Professional Exam

The context parameter is an instance of mlflow.pyfunc.model.ModelContext, which is a wrapper around the model metadata and artifacts. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files that are needed for the model inference. For example, the context parameter can be used to load a scikit-learn preprocessing model that was logged as an artifact with the MLflow model, and then apply it to the input data before passing it to the main model. The context parameter can also be used to access the model signature and the input example, if they were logged with the model. The context parameter is passed to the load_context and predict methods of the PythonModel class, which is asubclass of mlflow.pyfunc.PythonModel that defines the custom logic for loading and scoring the model. References:

mlflow.pyfunc Documentation, p. 1-2

mlflow.pyfunc.model Documentation, p. 1-2

Understanding PyFunc in MLflow, Part 2: Pyfunc Components, p. 1-2

python - How to load model from mlflow with custom predict without local file?, p. 1-2

The MLflow Model Registry provides a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage, model versioning, model aliasing, model tagging, and annotations. The Model Registry defines four model stages that represent the lifecycle of a model: None, Staging, Production, and Archived. Each model stage has a different meaning and purpose:

None: This is the default stage when a new model version is registered. It indicates that the model version is not ready for any downstream use cases and needs further development or testing.

Staging: This stage indicates that the model version is ready for testing or validation in a pre-production environment. It can be used for quality assurance, user acceptance testing, or performance evaluation before moving to production.

Production: This stage indicates that the model version is ready for deployment in a production environment. It can be used for serving predictions, scoring data, or supporting business decisions. Only one model version per registered model can be in the production stage at a time.

Archived: This stage indicates that the model version is no longer in active use and can be safely deleted or moved to a long-term storage. It can be used for historical or audit purposes, or for restoring a previous model version if needed.

References:

MLflow Model Registry Documentation, p. 1-2

Introducing the MLflow Model Registry - Databricks, p. 1-2

Manage MLflow Models — Databricks Documentation, p. 1-2

MLflow provides a utility function mlflow.models.signature.infer_signature that can infer the input schema and the output schema of a model from a sample dataset. The input schema and the output schema are represented as mlflow.types.schema.Schema objects, which define the names, data types, and shapes of the model inputs and outputs. The infer_signature function can be used for any model that implements a predict method that takes a pandas DataFrame or a numpy array as input and returns a pandas DataFrame, a pandas Series, a numpy array, or a list as output. The infer_signature function can also be used for models that implement a transform method that takes a pandas DataFrame as input and returns a pandas DataFrame as output. The infer_signature function can be used before logging the model with MLflow, as it does not require the model to be in MLflow format or registered with MLflow Model Registry. References:

MLflow Models Documentation, Model Signature and Input Example, p. 6-7

mlflow.types.schema Documentation, p. 1-2

Machine Learning Engineering with MLflow, Managing Model Signatures and Schemas, p. 1

Containers are a type of software packaging that can assist in real-time deployments by packaging software with its own application, tools, and libraries. Containers are isolated and portable environments that run on a shared operating system, but have their ownfile system, dependencies, configuration, and resources. Containers can help developers and operators to build, test, and deploy software faster, easier, and more reliably, as they ensure that the software runs the same way in any environment, whether it is a developer’s laptop, a test server, or a production cluster. Containers can also help to achieve scalability, high availability, and load balancing, as they can be easily created, destroyed, replicated, and orchestrated using tools such as Kubernetes, Docker Swarm, or Amazon ECS. Containers can also integrate with other tools and services, such as MLflow, Databricks, AWS CodeDeploy, and Google Cloud Deployment Manager, to enable end-to-end machine learning pipelines with tracking, reproducibility, and governance1234 References:

How to Package Your Application’s Infrastructure with Docker

21 open source DevOps tools (and what they do)

Microservices [What is it & Top Benefits]

9 Best Software Deployment Tools for 2023 (Paid & Free)

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc References:

Concept drift - Wikipedia

A Gentle Introduction to Concept Drift in Machine Learning

Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.

Data Drift vs. Concept Drift: What Is the Difference?

Feature drift, also known as data drift or covariate shift, occurs when the distribution of the input data changes over time, but the relationship between the input and the output remains the same. In this scenario, the expected temperature is an input feature that has changed its distribution, but the output (ice cream sales) still depends on the input in the same way. Feature drift can affect the performance of the model if the new data falls outside the range of the training data, as the model may not have learned how to handle such cases. Feature drift can be detected by monitoring the statistics of the input data over time and comparing them with the baseline statistics from the training data. References:

Databricks Machine Learning Documentation, Monitoring ML Models, Data Drift Detection, p. 2

A Gentle Introduction to Concept Drift in Machine Learning, Types of Concept Drift, p. 3

Understanding Data Drift and Model Drift: Drift Detection in Python, Types of Drift, p. 2

To restore the original scikit-learn model object, the correct method is mlflow.sklearn.load_model, which takes the model URI as an argument and returns a scikit-learn model. This method can load models with the sklearn flavor, which preserves the original attributes and methods of the model. The mlflow.load_model method is a generic method that can load models with any flavor, but it returns a pyfunc model, which does not have the feature_importances_ attribute. The client.list_artifacts method is used to list theartifacts associated with a run, not to load a model. The client.pyfunc.load_model method is similar to mlflow.load_model, but it takes a local file path instead of a model URI. The MLflow Experiments UI can show the logged parameters, metrics, and artifacts of a run, but it cannot show the model object itself. References:

MLflow Models

MLflow Python API

To create nested runs in MLflow, you need to specify the nested=True argument in the parent run, and omit it in the child runs. This will set a special tag (mlflow.utils.mlflow_tags.MLFLOW_PARENT_RUN_ID) in the child runs that indicates the parent run ID. This way, the UI will show the child runs under the parent run in a collapsible hierarchy. The indentation of the code blocks is not relevant, as long as the child runs are created within the context of the parent run. The run name parameter is also not relevant, as it does not affect the nesting of the runs. References: Understanding Parent and Child Runs in MLflow, Nested runs using MLflowClient, creating-child-runs-in-mlflow

The Model Registry is a centralized model store that allows you to manage the full lifecycle of MLflow Models. It provides model lineage, versioning, stage transitions, and annotations. You can use the Model Registry to discover and share models, collaborate on moving them from experimentation to production, integrate with approval and governance workflows, and monitor model deployments and performance1. The other options are incorrect because:

Option A: Models is a component of MLflow that allows you to manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms. Models is not a centralized model store, but rather a convention for packaging machine learning models that offers self-contained code, environments, and models2.

Option C: Model Serving is a feature of Databricks that allows you to host MLflow Models as REST endpoints. Model Serving is not a centralized model store, but rather a model deployment option that enables low-latency inference for real-time applications3.

Option D: Feature Store is a feature of Databricks that allows you to store, discover, and share features for machine learning. Feature Store is not a centralized model store, but rather a centralized feature store that enables feature reuse, consistency, and governance across ML projects4.

Option E: Experiments is a component of MLflow that allows you to track experiments to record and compare parameters and results. Experiments is not a centralized model store, but rather a logging service that stores runs, metrics,parameters, tags, and artifacts for each experiment. References: MLflow Model Registry, MLflow Models, MLflow Model Serving, Databricks Feature Store, [MLflow Tracking]