Important Databricks Machine Learning Associate Exam Questions

Answer: D

Explanation:

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.

Model Ensemble Techniques

Answer: C

Explanation:

To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, the RegressionEvaluator class is used. The RegressionEvaluator 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 the preds_df DataFrame is:

regression_evaluator = RegressionEvaluator( predictionCol='prediction', labelCol='actual', metricName='rmse' ) rmse = regression_evaluator.evaluate(preds_df)

This code creates an instance of RegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ('rmse'). It then evaluates the predictions in preds_df and assigns the resulting RMSE value to the rmse variable.

Options A and B incorrectly use BinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly uses BinaryClassificationEvaluator.

PySpark ML Documentation

Answer: A

Explanation:

The summary() function in PySpark's DataFrame API provides descriptive statistics which include count, mean, standard deviation, min, max, and quantiles for numeric columns. Here are the steps on how it can be used:

Import PySpark: Ensure PySpark is installed and correctly configured in the Databricks environment.

Load Data: Load the data into a Spark DataFrame.

Apply Summary: Use spark_df.summary() to generate summary statistics.

View Results: The output from the summary() function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles which approximate the interquartile range).

Reference

PySpark Documentation: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.summary.html

Answer: E

Explanation:

The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing pandas-based code while scaling to handle big data using Spark's distributed computing capabilities. pandas on Spark provides a similar API to pandas, making the transition smoother and faster compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or Spark SQL. Reference:

Databricks documentation on pandas API on Spark (formerly Koalas).

Answer: D

Explanation:

Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the errors of the previous ones. This sequential dependency makes it difficult to parallelize the training of trees in gradient boosting, as each step relies on the results from the preceding step. Parallelization in this context would undermine the core methodology of the algorithm, which depends on sequentially improving the model's performance with each iteration. Reference:

Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).

Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each new model corrects the errors made by the previous ones. This sequential dependency means that each iteration requires the results of the previous iteration to make corrections. Here is a step-by-step explanation of why this makes parallelization challenging:

Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the residual errors of the previous trees. This requires the model to complete one iteration before starting the next.

Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions made by the previous models. Therefore, the model must wait until the previous tree has been fully trained and evaluated before starting to train the next tree.

Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training process. Unlike algorithms that process data independently in each step (e.g., random forests), gradient boosting cannot easily distribute the work across multiple processors or cores for simultaneous execution.

This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize effectively.

Reference

Gradient Boosting Machine Learning Algorithm

Understanding Gradient Boosting Machines