Databricks-Machine-Learning-Associate Databricks Certified Machine Learning Associate Exam Practice Test

Answer : D

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.

Reference

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

Answer : D

By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:

Identify Missing Values: Determine which features contain missing values.

Impute Missing Values: Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.

Create Indicator Variables: For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.

Data Integration: Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.

Model Adjustment: Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.

Reference

'Feature Engineering for Machine Learning' by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.

Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html

Answer : B

Spark ML (part of Apache Spark's MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark.

Reference

Apache Spark MLlib Guide: https://spark.apache.org/docs/latest/ml-guide.html

Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here's a detailed explanation of how Spark ML can be used:

Hyperparameter Tuning with CrossValidator: Spark ML includes the CrossValidator and TrainValidationSplit classes, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster.

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Define the model

model = ...

# Create a parameter grid

paramGrid = ParamGridBuilder() \

.addGrid(model.hyperparam1, [value1, value2]) \

.addGrid(model.hyperparam2, [value3, value4]) \

.build()

# Define the evaluator

evaluator = BinaryClassificationEvaluator()

# Define the CrossValidator

crossval = CrossValidator(estimator=model,

estimatorParamMaps=paramGrid,

evaluator=evaluator,

numFolds=3)

Parallel Execution: Spark distributes the tasks of training models with different hyperparameters across the cluster's nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously.

Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations.

Reference

Apache Spark MLlib Documentation

Hyperparameter Tuning in Spark ML

Answer : B

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

Reference

Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

Answer : E

The correct method to randomly split a Spark DataFrame into training and test sets is by using the randomSplit method. 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. Reference:

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

Answer : A

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:

OverallCVRMSE=10.0+12.0+17.03=39.03=13.0OverallCVRMSE=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. Reference:

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

Answer : E

When the goal is to maximize the identification of positive cases in a classification task, the metric of interest is Recall. 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. Reference:

Classification Metrics in Machine Learning (Understanding Recall).