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Databricks Databricks-Machine-Learning-Associate Test Dumps

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Question # 1



Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?
A. Keras
B. pandas
C. PvTorch
D. Spark ML
E. Scikit-learn





Question # 2



Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
A. F1
B. R-squared
C. MAE
D. MSE





Question # 3



A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day. Which approach should the data scientist take to complete this task?
A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
B. They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.
C. They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes.
D. They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.





Question # 4



A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry. From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?
A. The home page of the MLflow Model Registry
B. The experiment page in the Experiments observatory
C. The model version page in the MLflow ModelRegistry
D. The model page in the MLflow Model Registry





Question # 5



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





Question # 6



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





Question # 7



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





Question # 8



Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?
A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
B. pandas API on Spark DataFrames are more performant than Spark DataFrames
C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames





Question # 9



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.





Question # 10



A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run. Which of the following approaches can the team use to identify which task is the cause of the failure?
A. Run each notebook interactively
B. Review the matrix view in the Job's runs
C. Migrate the Job to a Delta Live Tables pipeline
D. Change each Task’s setting to use a dedicated cluster





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Databricks Certified Machine Learning Associate Exam Dumps


Exam Code: Databricks-Machine-Learning-Associate
Exam Name: Databricks Certified Machine Learning Associate

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