Databricks Databricks-Machine-Learning-Associate Reliable Braindumps Sheet | Databricks-Machine-Learning-Associate Top Questions

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
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 # 39
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 automatically prune overfit trees
B. Spark ML decision trees test binned features values as representative split candidates
C. Spark ML decision trees test more split candidates in the splitting algorithm
D. Spark ML decision trees test every feature variable in the splitting algorithm
E. Spark ML decision trees test a random sample of feature variables in the splitting algorithm

Answer: B

Explanation:
One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.
Reference:
Spark MLlib Documentation (Decision Trees and Quantile Binning).

NEW QUESTION # 40
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?

A. It does not fit the imputer on the data to create an ImputerModel.
B. The inputCols and outputCols need to be exactly the same.
C. The fit method needs to be called instead of transform.
D. It does not impute both the training and test data sets.

Answer: A

Explanation:
In the provided code block, the Imputer object is created but not fitted on the data to generate an ImputerModel. The transform method is being called directly on the Imputer object, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.
Corrected code:
imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df) # Fit the imputer to the data imputed_features_df = imputer_model.transform(features_df) # Transform the data using the fitted imputer Reference:
PySpark ML Documentation

NEW QUESTION # 41
A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

A. Model deployment
B. Exploratory data analysis
C. Model tuning
D. Model evaluation

Answer: B

Explanation:
AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.
Reference
Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html

NEW QUESTION # 42
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?

A. 39.0
B. 12.0
C. 13.0
D. 10.0
E. 17.0

Answer: C

Explanation:
To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:
Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0=339.0=13.0 Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.
Reference:
Cross-validation in Regression (Understanding Cross-Validation Metrics).

NEW QUESTION # 43
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Databricks Databricks-Machine-Learning-Associate Reliable Braindumps Sheet | Databricks-Machine-Learning-Associate Top Questions

The Best Accurate Databricks-Machine-Learning-Associate Reliable Braindumps Sheet & Passing Databricks-Machine-Learning-Associate Exam is No More a Challenging Task

Databricks Certified Machine Learning Associate Exam Sample Questions (Q38-Q43):

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