Newest Databricks-Machine-Learning-Associate Practice Questions at Zero Cost

Get a glimpse of the real Databricks-Machine-Learning-Associate certification exam challenges with our free Databricks-Machine-Learning-Associate practice test questions.

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:

q1_Databricks-Machine-Learning-Associate

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?

AThe process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode

BThe process will leak data from the training set to the test set during the evaluation phase

CThe process will be unable to parallelize tuning due to the distributed nature of pipeline

DThe process will leak data prep information from the validation sets to the training sets for each model

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

q2_Databricks-Machine-Learning-Associate

Which of the following suggestions should the team include in their guidelines?

AThe F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.

BThe F1 score should be utilized over accuracy when there are greater than two classes in the target variable.

CThe F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

DThe F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.

Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

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?

AA holdout set is not necessary when using a train-validation split

BReproducibility is achievable when using a train-validation split

CFewer hyperparameter values need to be tested when using a train-validation split

DBias is avoidable when using a train-validation split

EFewer models need to be trained when using a train-validation split

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?

AChange the number of compute nodes to be half or less than half of the number of evaluations.

BChange the number of compute nodes and the number of evaluations to be much larger but equal.

CChange the iterative optimization algorithm used to facilitate the tuning process.

DChange the number of compute nodes to be double or more than double the number of evaluations.

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