Databricks Databricks Certified Machine Learning Associate Exam Databricks-Machine-Learning-Associate Exam Dumps: Updated Questions & Answers (October 2025)

To use the pandas API on Spark, the data scientist can run the following code block:

importpyspark.pandasasps df = ps.DataFrame(spark_df)

This code imports the pandas API on Spark and converts the Spark DataFramespark_dfinto a pandas-on-Spark DataFrame, allowing the data scientist to use familiar pandas functions for further feature engineering.

References:

Databricks documentation on pandas API on Spark: pandas API on Spark

Using an iterator in thepandas_udfensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.

References:

Databricks documentation on pandas UDFs: Pandas UDFs

To address the concern about standardizing features prior to splitting the data, the correct approach is to use the Pipeline API to ensure that only the training data's summary statistics are used to standardize the test data. This is achieved by fitting the StandardScaler (or any scaler) on the training data and then transforming both the training and test data using the fitted scaler. This approach prevents information leakage from the test data into the model training process and ensures that the model is evaluated fairly.References:

Best Practices in Preprocessing in Spark ML (Handling Data Splits and Feature Standardization).

The user wants to specify a search space for hyperparameters and let the tuning process randomly select values.GridSearchCVsystematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming.RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.

References

Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

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).

To improve the likelihood of a more accurate model, the data scientist can increasenum_evalsto 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.

References:

Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

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

Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.

References:

Parallel Computing Concepts

To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed topandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.

References

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

For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.

References:

Databricks documentation on linear regression: Linear Regression in Spark ML