Databricks Databricks-Machine-Learning-Associate - Databricks Certified Machine Learning Associate Exam

Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark’s distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets.

References:

Databricks documentation on Spark MLlib: Spark MLlib

The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.

References

Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.References:

Feature Engineering Techniques (One-Hot Encoding).

When the goal is to maximize the identification of positive cases in a classification task, the metric of interest isRecall. Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where missing a positive case (false negative) has serious implications, such as in medical diagnostics. The other metrics like Precision, RMSE, and Accuracy serve different aspects of performance measurement and are not specifically focused on maximizing the detection of positive cases alone.References:

Classification Metrics in Machine Learning (Understanding Recall).

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.References:

Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case,loyalty_tieris the only categorical column that should be imputed using the most common value.customer_idis a unique identifier and should not be imputed, whilespendandunitsare numerical columns that should typically be imputed using the mean or median values, not the mode.

References:

Databricks documentation on missing value imputation: Handling Missing Data

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To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

mlflow.register_model(f"runs:/{run_id}/model","best_model")

This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

References

MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

To filter rows in a Spark DataFrame based on a condition, thefiltermethod is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses thefiltermethod along with thecolfunction frompyspark.sql.functions.

Correct code:

frompyspark.sql.functionsimportcol filtered_df = spark_df.filter(col("discount") <=0)

Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of thecolfunction.

References:

PySpark SQL Documentation

To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:

Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0​=339.0​=13.0

Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.References:

Cross-validation in Regression (Understanding Cross-Validation Metrics).

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