Best Databricks Machine Learning Associate Exam Dumps (V8.02) – Tackle Your Databricks Certified Machine Learning Associate Exam with Confidence

1. A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically.

Which of the following lines of code will return the metadata description?

2. A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0.

Which of the following code blocks will accomplish this task?

3. A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.

Which of the following classification metrics should be used to evaluate the model?

4. In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

5. A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.

Which of the following approaches can they take to include as much information as possible in the feature set?

6. A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.

Which of the following lines of code can the data scientist run to accomplish the task?

7. 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?

8. 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 by comparing 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?

9. 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?

10. A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage.

As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.

Which of the following is a negative consequence of the approach suggested by the colleague?

11. What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

12. A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.

Which of the following describes why?

13. A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.

They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

14. A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.

They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

15. Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

16. Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

17. 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?

18. A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

19. 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?

20. A data scientist is using Spark ML to engineer features for an exploratory machine learning project.

They decide they want to standardize their features using the following code block:

Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.

Which of the following changes can the data scientist make to address the concern?

21. A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.

Which of the following feature engineering tasks will be the least efficient to distribute?

22. Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

23. A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

24. A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.

The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

25. 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?