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Databricks Databricks-Machine-Learning-Associate資格復習テキスト、Databricks-Machine-Learning-Associateテスト内容

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持ってきた製品があなたにふさわしくないと感じることはよくありますか？ Databricks-Machine-Learning-Associate学習ガイドを使用することに決めた場合、問題に遭遇することは決してないことを伝えたいと思います。私たちのDatabricks-Machine-Learning-Associate学習教材は、あなたが期待できない高品質を持っています。 Databricks-Machine-Learning-Associate学習教材のガイダンスで経験を積むと、以前よりも短時間で過ごすことができ、明らかに進歩を感じることができます。また、Databricks-Machine-Learning-Associateのテストクイズは、進歩に役立つことがわかります。。

>> Databricks Databricks-Machine-Learning-Associate資格復習テキスト <<

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Databricks Certified Machine Learning Associate Exam 認定 Databricks-Machine-Learning-Associate 試験問題 (Q31-Q36):

質問 # 31
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 RMSE is an invalid evaluation metric for regression problems
B. The second model is much more accurate than the first model
C. The first model is much more accurate than the second model
D. The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE
E. The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE

正解：A

解説：
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.

質問 # 32
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?

正解：B

解説：
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).

質問 # 33
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

A. The data will be distributed across multiple executors during the inference process
B. The model will be limited to a single executor preventing the data from being distributed
C. The model only needs to be loaded once per executor rather than once per batch during the inference process
D. The data will be limited to a single executor preventing the model from being loaded multiple times

正解：C

解説：
Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.
Reference:
Databricks documentation on pandas UDFs: Pandas UDFs

質問 # 34
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?

正解：A

解説：
To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, the RegressionEvaluator class is used. The RegressionEvaluator is specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.
The correct code block to compute RMSE from the preds_df DataFrame is:
regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse" ) rmse = regression_evaluator.evaluate(preds_df) This code creates an instance of RegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions in preds_df and assigns the resulting RMSE value to the rmse variable.
Options A and B incorrectly use BinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly uses BinaryClassificationEvaluator.
Reference:
PySpark ML Documentation

質問 # 35
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 tuning
B. Exploratory data analysis
C. Model evaluation
D. Model deployment

正解：B

解説：
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

質問 # 36
......

学歴は実力と等しくなく、能力とも等しくないです。本当の能力は実践で鍛えたもので、学歴と直接な関係がないです。「私はだめです。」と思わないでください。DatabricksのDatabricks-Machine-Learning-Associate試験に申し込んだあなたは自分が合格できないなんてを心配だったら、JpshikenのDatabricksのDatabricks-Machine-Learning-Associate試験トレーニング資料を利用してください。学歴がどんなに高くて、能力がどんなに低くても、首尾よく試験に合格することができます。

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