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ML Data Scientist Databricks Certified Machine Learning Associate Exam

Databricks Certified Machine Learning Associate Exam

Last Update Apr 23, 2025
Total Questions : 74

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Questions 2

A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).

Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

A)

Questions 2

B)

Questions 2

C)

Questions 2

D)

Questions 2

Options:

A.  

OptionA

B.  

Option B

C.  

Option C

D.  

Option D

Discussion 0
Questions 3

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

Questions 3

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

Options:

A.  

Increase num_evals to 100

B.  

Change fmin() to fmax()

C.  

Change sparkTrials() to Trials()

D.  

Change tpe.suggest to random.suggest

Discussion 0
Kingsley
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Questions 4

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?

Options:

A.  

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

B.  

Reproducibility is achievable when using a train-validation split

C.  

Fewer hyperparameter values need to be tested when usinga train-validation split

D.  

Bias is avoidable when using a train-validation split

E.  

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

Discussion 0
Questions 5

A data scientist has created a linear regression model that useslog(price)as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFramepreds_df.

They are using the following code block to evaluate the model:

regression_evaluator.setMetricName("rmse").evaluate(preds_df)

Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable withprice?

Options:

A.  

They should exponentiate the computed RMSE value

B.  

They should take the log of the predictions before computing the RMSE

C.  

They should evaluate the MSE of the log predictions to compute the RMSE

D.  

They should exponentiate the predictions before computing the RMSE

Discussion 0

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