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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

Topic 2

Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

Topic 3

ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

Topic 4

Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_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 with price?

A. They should exponentiate the predictions before computing the RMSE
B. They should evaluate the MSE of the log predictions to compute the RMSE
C. They should exponentiate the computed RMSE value
D. They should take the log of the predictions before computing the RMSE

Answer: A

Explanation:
When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.
Reference:
Databricks documentation on regression evaluation: Regression Evaluation

NEW QUESTION # 64
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?

A. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
B. Replace the GridSearchCV operation with RandomizedSearchCV
C. Replace the GridSearchCV operation with ParameterGrid
D. Replace the GridSearchCV operation with cross_validate
E. Replace the random_state=0 argument with random_state=1

Answer: B

Explanation:
The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming. RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

NEW QUESTION # 65
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. Spark ML
B. Scikit-learn
C. Keras
D. pandas
E. PvTorch

Answer: A

Explanation:
Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).

NEW QUESTION # 66
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Answer: C

Explanation:
The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to "rmse". Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol="prediction" and labelCol="actual". Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.
Reference:
Spark ML documentation (Using RegressionEvaluator to Compute RMSE).

NEW QUESTION # 67
A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model in parallel. They elect to use the Hyperopt library to facilitate this process.
Which of the following Hyperopt tools provides the ability to optimize hyperparameters in parallel?

A. search_space
B. fmin
C. objective_function
D. SparkTrials
E. quniform

Answer: D

Explanation:
The SparkTrials class in the Hyperopt library allows for parallel hyperparameter optimization on a Spark cluster. This enables efficient tuning of hyperparameters by distributing the optimization process across multiple nodes in a cluster.
from hyperopt import fmin, tpe, hp, SparkTrials search_space = { 'x': hp.uniform('x', 0, 1), 'y': hp.uniform('y', 0, 1) } def objective(params): return params['x'] ** 2 + params['y'] ** 2 spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials) Reference:
Hyperopt Documentation

NEW QUESTION # 68
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