Databricks-Machine-Learning-Associate EXAM DUMPS WITH GUARANTEED SUCCESS
Databricks-Machine-Learning-Associate EXAM DUMPS WITH GUARANTEED SUCCESS
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Databricks Certified Machine Learning Associate Exam Sample Questions (Q21-Q26):
NEW QUESTION # 21
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 # 22
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
- A. String indexing
- B. Target encoding
- C. One-hot encoding
- D. Categorical
- E. Leave-one-out encoding
Answer: C
Explanation:
The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.
Reference:
Feature Engineering Techniques (One-Hot Encoding).
NEW QUESTION # 23
A data scientist is using the following code block to tune hyperparameters for a machine learning model:
Which change can they make the above code block to improve the likelihood of a more accurate model?
- A. Increase num_evals to 100
- B. Change tpe.suggest to random.suggest
- C. Change fmin() to fmax()
- D. Change sparkTrials() to Trials()
Answer: A
Explanation:
To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.
Reference:
Databricks documentation on hyperparameter tuning: Hyperparameter Tuning
NEW QUESTION # 24
A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?
- A. Spark SQL
- B. pandas API on Spark
- C. PySpark DataFrame API
- D. Feature Store
Answer: B
Explanation:
The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark's distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark
NEW QUESTION # 25
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. The inputCols and outputCols need to be exactly the same.
- B. The fit method needs to be called instead of transform.
- C. It does not impute both the training and test data sets.
- D. It does not fit the imputer on the data to create an ImputerModel.
Answer: D
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 # 26
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