Databricks Updated Databricks-Machine-Learning-Professional Exam Questions and Answers by bradley

A two-way Chi-squared test is a statistical test that can be used to compare the frequencies of categorical variables across two or more groups. In this case, the engineer can use a two-way Chi-squared test to compare the frequencies of the categorical input variable values across different time periods (e.g., months, quarters, etc.) and see if there is a significant difference in the proportion of missing values. A significant difference would indicate that the missing values are not randomly distributed and may affect the performance of the machine learning model. References:

• Databricks Machine Learning Professional Exam Guide, Section 4: Solution and Data Monitoring, p. 5
• Databricks Machine Learning Documentation, Monitoring ML Models, Data Drift Detection, p. 3

MLflow Model flavors are a key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with models from any ML library without having to integrate each tool with each library. MLflow defines a set of built-in model flavors for popular ML frameworks, such as scikit-learn, TensorFlow, PyTorch, etc. Each flavor defines a standard format for model data and a common interface for model inference. For example, the python_function flavor allows any model to be loaded as a Pythonfunction that can be applied to input data. MLflow also supports custom model flavors for user-defined models or community-developed flavors. MLflow Model flavors enable model portability and interoperability across different platforms and environments12 References:

• MLflow Models — MLflow 2.9.1 documentation
• Models, Flavors, and PyFuncs in MLflow

Feature drift is a change in the distribution of the input data over time, which can affect the performance and accuracy of the model. Monitoring feature drift is important to ensure that the model is still valid and reliable for the current data. One simple and low-cost method of monitoring numeric feature drift is to track the summary statistics trends of the input features, such as mean, standard deviation, minimum, maximum, etc. These statistics can be computed easily and efficiently, and can provide a quick overview of the changes in the data distribution. If the summary statistics deviate significantly from the baseline values, it may indicate a feature drift. However, summary statistics trends may not capture all the nuances of the data distribution, such as outliers, multimodality, skewness, etc. Therefore, other methods, such as statistical tests or visualizations, may be needed to complement the summary statistics trends and provide a more comprehensive analysis of the feature drift123 References:

• Monitoring Feature Drift - Databricks
• Monitor feature attribution skew and drift | Vertex AI | Google Cloud
• Monitoring Model Drift - 6 Different Methodologies - Qualdo

MLflow Model Serving allows you to deploy MLflow models for real-time serving via REST API. When you enable Model Serving for a registered model, all model versions in the Staging and Production stages are automatically deployed. You can also manually deploy model versions in other stages, such as None or Archived, by using the UI or the API. However, only the model versions in the Staging and Production stages are automatically deployed by default. References: MLflow Model Serving, Deploy MLflow models