Databricks-Machine-Learning-Professionalの無料問題集（62題）、Databricks-Machine-Learning-Professional認定試験参考書

Databricks Databricks-Machine-Learning-Professional 問題集

試験コード：Databricks-Machine-Learning-Professional

試験名称：Databricks Certified Machine Learning Professional

最近更新時間：2025-09-11

問題と解答：全62問

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質問 1：
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?
A.

C. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
D.

正解：A

質問 2：
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?
A. Replace POST with GET in the call to http request
B. Replace list with webhooks in the endpoint URL
C. Replace POST with PUT in the call to http request
D. Replace list with view in the endpoint URL
E. There are no necessary changes
正解：B

質問 3：
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. The version number of the model version in Production is necessary to complete this task.
B. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
C. https://<databricks-instance>/model/recommender/Production/invocations
D. https://<databricks-instance>/model-serving/recommender/Production/invocations
E. https://<databricks-instance>/model/recommender/stage-production/invocations
正解：A

質問 4：
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?
A.

正解：E

質問 5：
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?
A. spark.conf.set("autologging", True)
B. It is not possible to automatically log MLflow runs.
C. mlflow.spark.autolog()
D. mlflow.sklearn.autolog()
E. mlflow.autolog()
正解：A

質問 6：
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?
A.

正解：D

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Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
トピック 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
トピック 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 5	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
トピック 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
トピック 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
トピック 8	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
トピック 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
トピック 10	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
トピック 11	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

参照：https://www.databricks.com/learn/certification/machine-learning-professional

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