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Databricks Certified Machine Learning Associate Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
A data scientist is using the following code block to tune hyperparameters for a machine learning model:

Which change can they make the above code block to improve the likelihood of a more accurate model?

A. Change fmin() to fmax()
B. Change tpe.suggest to random.suggest
C. Change sparkTrials() to Trials()
D. Increase num_evals to 100

Answer: D

Explanation:
To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 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.
Reference:
Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

NEW QUESTION # 49
A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A. Reproducibility is achievable when using a train-validation split
B. A holdout set is not necessary when using a train-validation split
C. Fewer models need to be trained when using a train-validation split
D. Fewer hyperparameter values need to be tested when using a train-validation split
E. Bias is avoidable when using a train-validation split

Answer: C

NEW QUESTION # 50
A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

A. The process will leak data from the training set to the test set during the evaluation phase
B. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
C. The process will be unable to parallelize tuning due to the distributed nature of pipeline
D. The process will leak data prep information from the validation sets to the training sets for each model

Answer: B

Explanation:
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.
If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.
Reference:
Databricks documentation on cross-validation: Cross Validation

NEW QUESTION # 51
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

A. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
B. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
C. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
D. One-hot encoding is dependent on the target variable's values which differ for each apaplication.

Answer: C

Explanation:
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.
Reference:
Databricks documentation on feature engineering: Feature Engineering

NEW QUESTION # 52
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Singular value decomposition
B. Logistic regression
C. Least-squares method
D. Iterative optimization

Answer: D

Explanation:
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.
Reference:
Databricks documentation on linear regression: Linear Regression in Spark ML

NEW QUESTION # 53
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