Free PDF Quiz Databricks - Databricks-Machine-Learning-Associate - Databricks Certified Machine Learning Associate Exam Latest Valid Vce Dumps

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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.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

A. The home page of the MLflow Model Registry
B. The model page in the MLflow Model Registry
C. The model version page in the MLflow Model Registry
D. The experiment page in the Experiments observatory

Answer: C

Explanation:
The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.
Reference
Databricks documentation on MLflow Model Registry: https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

NEW QUESTION # 60
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

A. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
B. pandas API on Spark DataFrames are more performant than Spark DataFrames
C. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
D. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

Answer: A

Explanation:
The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark's underlying capabilities.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark

NEW QUESTION # 61
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 first model is much more accurate than the second model
B. The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE
C. The RMSE is an invalid evaluation metric for regression problems
D. The second model is much more accurate than the first model
E. The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE

Answer: C

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 # 62
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. SparkTrials
C. objective_function
D. quniform
E. fmin

Answer: B

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 # 63
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(run_id, "best_model")
C. millow.register_model(f"runs:/{run_id)/model")
D. mlflow.register_model(f"runs:/{run_id}/best_model", "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 # 64
......

The Databricks sector is an ever-evolving and rapidly growing industry that is crucial in shaping our lives today. With the growing demand for skilled Databricks professionals, obtaining Databricks Certified Machine Learning Associate Exam (Databricks-Machine-Learning-Associate) certification exam has become increasingly important for those who are looking to advance their careers and stay competitive in the job market. Individuals who hold Databricks Certified Machine Learning Associate Exam (Databricks-Machine-Learning-Associate) certification exam demonstrate to their employers and clients that they have the knowledge and skills necessary to succeed in the Databricks-Machine-Learning-Associate exam.

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FREE PDF QUIZ DATABRICKS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE - DATABRICKS CERTIFIED MACHINE LEARNING ASSOCIATE EXAM LATEST VALID VCE DUMPS

Free PDF Quiz Databricks - Databricks-Machine-Learning-Associate - Databricks Certified Machine Learning Associate Exam Latest Valid Vce Dumps