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Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲:
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>> Databricks-Machine-Learning-Professional合格体験談 <<
完璧なDatabricks-Machine-Learning-Professional合格体験談 & 合格スムーズDatabricks-Machine-Learning-Professional試験 | 検証するDatabricks-Machine-Learning-Professional模擬練習
Databricks-Machine-Learning-Professional試験はDatabricksの認定試験の一つですが、もっとも重要なひとつです。DatabricksのDatabricks-Machine-Learning-Professionalの認定試験に合格するのは簡単ではなくて、Tech4ExamはDatabricks-Machine-Learning-Professional試験の受験生がストレスを軽減し、エネルギーと時間を節約するために専門研究手段として多様な訓練を開発して、Tech4Examから君に合ったツールを選択してください。
Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q18-Q23):
質問 # 18
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?
- A. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
- B. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute. - C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
- D. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
正解:A
解説:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.
質問 # 19
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?
- A. Jobs cannot be created programmatically
- B. AutoML APIs
- C. Databricks REST APIs
- D. MLflow APIs
- E. MLflow Client
正解:C
質問 # 20
Label drift occurs where there is a change in which element?
- A. The distribution of a target variable
- B. The distribution of the predicted target given by the model
- C. The relationship between input variables and target variables
- D. The distribution of an input variable
正解:A
解説:
Label drift refers to a change in the distribution of the target variable over time. This means the frequencies or proportions of classes or target values shift, which can impact model performance even if the input feature distributions remain unchanged.
質問 # 21
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?
- A. mlflow.sklearn.load_model(model_uri)
- B. mlflow.spark.load_model(model_uri)
- C. mlflow.spark.read_model(model_uri)
- D. mlflow.pyfunc.load_model(model_uri)
正解:B
解説:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.
質問 # 22
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. Which of the following code blocks can they use to accomplish the task?
- A.

- B.

- C.

- D.

- E.
正解:B
質問 # 23
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