Pass the Databricks ML Data Scientist Databricks-Machine-Learning-Associate Questions and answers with ExamsMirror

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.

References:

Databricks documentation on Random Forest: Random Forest in Spark ML

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

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 simplymapInPandas(predict), which specifies the UDF to use without additional arguments orincorrect function calls.References:

PySpark DataFrame documentation (Using mapInPandas with UDFs).

Among the options listed, calculating the true median for imputing missing feature values is the least efficient to distribute. This is because the true median requires knowledge of the entire data distribution, which can be computationally expensive in a distributed environment. Unlike mean or mode, finding the median requires sorting the data or maintaining a full distribution, which is more intensive and often requires shuffling the data across partitions.

References

Challenges in parallel processing and distributed computing for data aggregation like median calculation:https://www.apache.org

In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers likeStringIndexer,VectorAssembler, andStandardScaler.

References:

Databricks documentation on transformers: Transformers in Spark ML

AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.

References

Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html

The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use theRegressionEvaluatorclass withmetricNameset to "rmse". Given the schema ofpreds_dfwith columnspredictionandactual, the correct evaluator setup will specifypredictionCol="prediction"andlabelCol="actual". Thus, the appropriate code block (Option C in your list) that usesRegressionEvaluatorto compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.References:

Spark ML documentation (Using RegressionEvaluator to Compute RMSE).

The correct method to randomly split a Spark DataFrame into training and test sets is by using therandomSplitmethod. This method allows you to specify the proportions for the split as a list of weights and returns multiple DataFrames according to those weights. This is directly intended for splitting DataFrames randomly and is the appropriate choice for preparing data for training and testing in machine learning workflows.References:

Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).

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).

To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specifynested=Truewhen starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.References:

MLflow Documentation (Managing Nested Runs).