Databricks-Machine-Learning-Professional日本語対策 & Databricks-Machine-Learning-Professional日本語問題集

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

Section	Weight	Objectives
Model Deployment	12%	- Custom model serving - Model rollout and version management - Deployment strategies
Model Development	44%	- Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Advanced MLflow usage - Scalable ML pipelines with SparkML
ML Ops	44%	- Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles - Automated retraining workflows

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Databricks-Machine-Learning-Professional日本語問題集、Databricks-Machine-Learning-Professional模擬練習

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q149-Q154):

質問 # 149
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

A. https://<databricks-instance>/model-serving/recommender/Production/invocations
B. The version number of the model version in Production is necessary to complete this task.
C. https://<databricks-instance>/model/recommender/stage-production/invocations
D. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
E. https://<databricks-instance>/model/recommender/Production/invocations

正解：B

質問 # 150
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
B. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
C. All of these statements
D. None of these statements
E. Because the streaming deployment is always on, all types of data must be handled without producing an error

正解：A

質問 # 151
A Data Scientist has trained a regression model using Spark's MLlib to predict housing prices.
The model needs to be evaluated to determine how well it predicts on a testing dataset. The Data Scientist wants to use a standard regression metric for the evaluation. They also want to evaluate the model with the least processing overhead required. Which approach will suit their needs?

A. Use the MLlib's RankingEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
B. Convert the testing dataset to a pandas DataFrame and use scikit-learn's root_mean_squared_error function to calculate the root mean squared error on the testing dataset.
C. Implement a Spark UDF to calculate the root mean squared error for each row in the testing dataset and aggregate these values to obtain the overall RMSE score.
D. Use the MLlib's RegressionEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.

正解：D

解説：
RegressionEvaluator is the Spark ML-native, distributed evaluation API for regression models.
Setting metricName to rmse computes root mean squared error directly on the distributed test DataFrame with minimal overhead, avoiding unnecessary data movement or custom computation logic.

質問 # 152
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

正解：B

質問 # 153
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables. Which of the following tools can the machine learning engineer use to assess their theory?

A. Jenson-Shannon distance
B. None of these
C. One-way Chi-squared Test
D. Kolmogorov-Smirnov (KS) test
E. Two-way Chi-squared Test

正解：C

質問 # 154
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