Databricks-Machine-Learning-Professional勉強の資料 & Databricks-Machine-Learning-Professional模試エンジン

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

Section	Objectives
Machine Learning Workflow on Databricks	- Data preparation and feature engineering 1. Data ingestion and cleaning in Databricks 2. Feature engineering techniques - End-to-end ML pipelines 1. Pipeline construction and orchestration 2. Reusable ML workflows
Model Deployment and Serving	- Model deployment strategies 1. Batch inference vs real-time inference 2. Databricks Model Serving
MLOps, Monitoring, and Governance	- Model monitoring 1. Performance tracking in production 2. Data drift detection - Governance and compliance 1. Model lifecycle governance 2. Feature Store usage and management
MLflow and Experiment Tracking	- Experiment management 1. Tracking runs and parameters 2. Model comparison and selection - Model registry 1. Versioning and lifecycle management
Machine Learning Models and Algorithms	- Supervised learning methods 1. Model evaluation metrics 2. Classification and regression models - Unsupervised learning methods 1. Clustering techniques 2. Dimensionality reduction

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Databricks Databricks-Machine-Learning-Professional Exam | Databricks-Machine-Learning-Professional勉強の資料 - 信頼できるプロバイダ Databricks-Machine-Learning-Professional: Databricks Certified Machine Learning Professional 試験

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q51-Q56):

質問 # 51
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?

正解：A

質問 # 52
A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

A. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
B. It registers a new model called model_name in the MLflow Model Registry.
C. It registers the new version of the existing model_name model in the MLflow Model Registry.
D. It identifies the name of the logged model in the MLflow Experiment.

正解：B

解説：
The parameter registered_model_name=model_name in mlflow.pyfunc.log_model() automatically creates and registers a new model in the MLflow Model Registry if it doesn't already exist. Since this is the first time the model is being run, MLflow will register a new model named model_name and log the current model as version 1 of that registered model.

質問 # 53
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Experiments
B. Models
C. Feature Store
D. Model Registry
E. Model Serving

正解：E

質問 # 54
Which of the following is a simple statistic to monitor for categorical feature drift?

A. Percentage of missing values
B. Mode
C. Mode, number of unique values, and percentage of missing values
D. Number of unique values
E. None of these

正解：C

質問 # 55
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They have created a model signature model_signature and a few example input records input_records. Which block of code can be used to log all of this information?

A.
B. There is no way to log a model signature.
C. mlflow.sklearn.log_model(model, "model")
mlflow.log_signature(model_signature)
mlflow.log_input(input_records)
D.

正解：D

解説：
The code block correctly uses the mlflow.sklearn.log_model() function with both signature and input_example parameters.
signature=model_signature defines the expected model input and output schema, ensuring reproducibility and validation during inference.
input_example=input_records provides a sample input that helps users understand the model's expected input format.

質問 # 56
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