Databricks-Machine-Learning-Professional最も有効な質問と解答で勉強

In a Spark ML Pipeline, what is the role of a Transformer?

• A. Converts datasets into new datasets
• B. Trains models
• C. Fits data
• D. Evaluates models

Which MLflow function registers a model to the registry?

• A. mlflow.register_model()
• B. mlflow.log_param()
• C. mlflow.create_model()
• D. mlflow.deploy_model()

A Machine Learning Engineer has deployed a fraud detection model that processes 10,000 transactions per hour. The model was trained on data from Q1 2024, but it's now Q4 2024. The ML team notices three concerning trends: (1) the model's precision has dropped from 92% to
78% over the past month, (2) the average transaction amount in recent data has increased from
$150 to $220 and (3) the relationship between transaction frequency and fraud likelihood has weakened significantly due to new payment methods being introduced. The engineer needs to implement a monitoring solution that can detect the root cause of the performance degradation, identify why the precision dropped, and be able to do this at the scale needed. Which monitoring pipeline component will do this?

• A. Logging and Monitoring Pipeline because it captures all prediction requests and can identify patterns in the fraud predictions.
• B. Drift Detection Pipeline because it can identify both the data drift (transaction amount changes) and concept drift (weakened relationship between features and target).
• C. Model Performance Monitoring Pipeline because it directly tracks precision metrics and can alert when thresholds are breached.
• D. Model Health Monitoring Pipeline because it ensures the infrastructure can handle the 10,000 transactions per hour load.

A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project". Which MLflow operation can the machine learning engineer use to accomplish this task?

• A. MlflowClient.get_model_version
• B. The machine learning engineer needs to create an entirely new MLflow Model Registry model
• C. mlflow.add_model_version
• D. MlflowClient.update_registered_model
• E. mlflow.register_model

A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

• A. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
• B. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.
• C. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
• D. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.