Databricks Databricks-Machine-Learning-Associate Dumps

D.   Spark ML

Answer DescriptionExplanation:

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.

References:

Spark MLlib documentation (Feature Engineering with Spark ML).

A.   F1

Answer DescriptionExplanation:

The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

References:

Databricks documentation on Feature Store: Feature Store in Databricks

A.   They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.

Answer DescriptionExplanation:

The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.

References:

Databricks documentation on Git integration: Databricks Repos

C.   The model version page in the MLflow ModelRegistry

Answer DescriptionExplanation:

The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.

References

Databricks documentation on MLflow Model Registry:
https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

E.   Spark ML decision trees test binned features values as representative split candidates

Answer DescriptionExplanation:

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.

References:

Spark MLlib Documentation (Decision Trees and Quantile Binning).

C.   When the features contain a lot of extreme outliers

Answer DescriptionExplanation:

Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.

References:

Data Imputation Techniques (Dealing with Outliers).

E.   The RMSE is an invalid evaluation metric for regression problems

Answer DescriptionExplanation:

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.

Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale. Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.

C.   pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

Answer DescriptionExplanation:

Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.

References

pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

C.   Change the iterative optimization algorithm used to facilitate the tuning process.

Answer DescriptionExplanation:

The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

References:

Hyperparameter Optimization with Hyperopt

B.   Review the matrix view in the Job's runs

Answer DescriptionExplanation:

To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach ismore efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.

References:

Databricks documentation on Jobs: Jobs in Databricks