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Databricks-Machine-Learning-Associate Practice Questions

Question # 1
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
  • 10.0
  • 12.0
  • 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?
A. 13.0
B. 17.0
C. 12.0
D. 39.0
E. 10.0


A. 13.0


Question # 2
Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?
A. TrainValidationSplit
B. DataFrame.where
C. CrossValidator
D. TrainValidationSplitModel
E. DataFrame.randomSplit


E. DataFrame.randomSplit


Question # 3
A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process. Which change could the data scientist make to improve their model accuracy over the course of their tuning process?
A. Change the number of compute nodes to be half or less than half of the number of evaluations.
B. Change the number of compute nodes and the number of evaluations to be much larger but equal.
C. Change the iterative optimization algorithm used to facilitate the tuning process.
D. Change the number of compute nodes to be double or more than double the number of evaluations.


C. Change the iterative optimization algorithm used to facilitate the tuning process.

Explanation:

The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

References:

Hyperparameter Optimization with Hyperopt

Question # 4
A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster. Which of the following approaches will guarantee a reproducible training and test set for each model?
A. Manually configure the cluster
B. Write out the split data sets to persistent storage
C. Set a speed in the data splitting operation
D. Manually partition the input data


B. Write out the split data sets to persistent storage

Explanation:

To ensure reproducible training and test sets, writing the split data sets to persistent storage is a reliable approach. This allows you to consistently load the same training and test data for each model run, regardless of cluster reconfiguration or other changes in the environment.

Correct approach:

Split the data.

Write the split data to persistent storage (e.g., HDFS, S3).

Load the data from storage for each model training session.

train_df, test_df = spark_df.randomSplit([0.8,0.2], seed=42)

train_df.write.parquet("path/to/train_df.parquet") test_df.write.parquet("path/to/test_df.parquet")# Later, load the datatrain_df = spark.read.parquet("path/to/train_df.parquet") test_df = spark.read.parquet("path/to/test_df.parquet")

References:

Spark DataFrameWriter Documentation

Question # 5
In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?
A. When the features are of the categorical type
B. When the features are of the boolean type
C. When the features contain a lot of extreme outliers
D. When the features contain no outliers
E. When the features contain no missingno values


C. When the features contain a lot of extreme outliers

Explanation:

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).


Question # 6
A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model bycomparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model. Which of the following possible explanations for this difference is invalid?
A. The second model is much more accurate than the first model
B. The data scientist failed to exponentiate the predictions in the second model prior tocomputingthe RMSE
C. The datascientist failed to take the logof the predictions in the first model prior to computingthe RMSE
D. The first model is much more accurate than the second model
E. The RMSE is an invalid evaluation metric for regression problems


E. The RMSE is an invalid evaluation metric for regression problems

Explanation:

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.

Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale. Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.


Question # 7
A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data. Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?
A. They can refactor their notebook to process the data in parallel.
B. They can refactor their notebook to use the PySpark DataFrame API.
C. They can refactor their notebook to use the Scala Dataset API.
D. They can refactor their notebook to use Spark SQL.
E. They can refactor their notebook to utilize the pandas API on Spark.


E. They can refactor their notebook to utilize the pandas API on Spark.


Question # 8
A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical. Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?
A. Spark ML decision trees test every feature variable in the splitting algorithm
B. Spark ML decision trees automatically prune overfit trees
C. Spark ML decision trees test more split candidates in the splitting algorithm
D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm
E. Spark ML decision trees test binned features values as representative split candidates


E. Spark ML decision trees test binned features values as representative split candidates

Explanation:

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.

References:

Spark MLlib Documentation (Decision Trees and Quantile Binning).


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