TorrentVCE Databricks Databricks-Machine-Learning-Associate Practice Test

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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	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.
Topic 4	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/model", "best_model")
B. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
C. millow.register_model(f"runs:/{run_id)/model")
D. mlflow.register_model(run_id, "best_model")

Answer: A

Explanation:
To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:
mlflow.register_model(f"runs:/{run_id}/model", "best_model")
This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.
Reference
MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

NEW QUESTION # 76
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 exponentiate the computed RMSE value
C. They should take the log of the predictions before computing the RMSE
D. They should evaluate the MSE of the log predictions to compute 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 # 77
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?

A. The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE
B. The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE
C. The first model is much more accurate than the second model
D. The RMSE is an invalid evaluation metric for regression problems
E. The second model is much more accurate than the first model

Answer: D

Explanation:
The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here's a breakdown of why the other statements are or are not valid:
Transformations and RMSE Calculation: If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.
Accuracy of Models: Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale.
Appropriateness of RMSE: RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.
Reference
"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.

NEW QUESTION # 78
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?

A. DataFrame.randomSplit
B. CrossValidator
C. DataFrame.where
D. TrainValidationSplit
E. TrainValidationSplitModel

Answer: A

Explanation:
The correct method to randomly split a Spark DataFrame into training and test sets is by using the randomSplit method. This method allows you to specify the proportions for the split as a list of weights and returns multiple DataFrames according to those weights. This is directly intended for splitting DataFrames randomly and is the appropriate choice for preparing data for training and testing in machine learning workflows.
Reference:
Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).

NEW QUESTION # 79
Which statement describes a Spark ML transformer?

A. A transformer is a learning algorithm that can use a DataFrame to train a model
B. A transformer is a hyperparameter grid that can be used to train a model
C. A transformer chains multiple algorithms together to transform an ML workflow
D. A transformer is an algorithm which can transform one DataFrame into another DataFrame

Answer: D

Explanation:
In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers like StringIndexer, VectorAssembler, and StandardScaler.
Reference:
Databricks documentation on transformers: Transformers in Spark ML

NEW QUESTION # 80
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TORRENTVCE DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE PRACTICE TEST

TorrentVCE Databricks Databricks-Machine-Learning-Associate Practice Test