DATABRICKS-MACHINE-LEARNING-ASSOCIATE Online Practice Questions and Answers

Correct Answer: B

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

Correct Answer: B

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much moreefficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation. References: PySpark Documentation on UDFs:https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_panda s.html#pandas-udfs-a-k-a-vectorized-udfs

Correct Answer: E

A train-validation split is often preferred over k-fold cross-validation (with k >

2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold). This

reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.

References:

Model Evaluation with Train-Test Split

Correct Answer: B

If the model object is passed to the estimator parameter ofCrossValidator and the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training

and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would

potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

Correct Answer: D

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different

conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.

References:

Introduction to Ensemble Learning:

https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/

Correct Answer: E

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).

Correct Answer: A

The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding. References: Databricks documentation on feature engineering: Feature Engineering

Correct Answer: B

To apply the Pandas UDFpredictto each record of a Spark DataFrame, you use themapInPandasmethod. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function

(predictin this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments orincorrect function calls.References:

PySpark DataFrame documentation (Using mapInPandas with UDFs).

Correct Answer: C

To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, theRegressionEvaluatorclass is used. TheRegressionEvaluator is specifically designed for regression tasks and can calculate various metrics,

including RMSE, based on the columns containing predictions and actual values. The correct code block to compute RMSE from thepreds_dfDataFrame is:

regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse") rmse = regression_evaluator.evaluate(preds_df) This code creates an instance ofRegressionEvaluator, specifying the prediction

and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions in preds_dfand assigns the resulting RMSE value to thermsevariable. Options A and B incorrectly useBinaryClassificationEvaluator, which is

not suitable for regression tasks. Option D also incorrectly usesBinaryClassificationEvaluator.

References:

PySpark ML Documentation

Correct Answer: E

One-hot encoding transforms categorical variables into a format that can be provided to machine learning algorithms to better predict the output. However, when done prematurely or universally within a feature repository, it can be

problematic:

Dimensionality Increase:One-hot encoding significantly increases the feature space, especially with high cardinality features, which can lead to high memory consumption and slower computation.

Model Specificity:Some models handle categorical variables natively (like decision trees and boosting algorithms), and premature one-hot encoding can lead to inefficiency and loss of information (e.g., ordinal relationships). Sparse Matrix

Issue:It often results in a sparse matrix where most values are zero, which can be inefficient in both storage and computation for some algorithms. Generalization vs. Specificity:Encoding should ideally be tailored to specific models and use

cases rather than applied generally in a feature repository.

References:

"Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson (CRC Press, 2019).