検証するDatabricks-Machine-Learning-Professional｜100％合格率のDatabricks-Machine-Learning-Professional復習問題集試験｜試験の準備方法Databricks Certified Machine Learning Professional模擬試験問題集

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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	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 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 3	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 4	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
トピック 5	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 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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
トピック 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	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 11	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

>> Databricks-Machine-Learning-Professional復習問題集 <<

Databricks-Machine-Learning-Professional模擬試験問題集 & Databricks-Machine-Learning-Professional勉強資料

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q133-Q138):

質問 # 133
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
They are using the following code block as part of their solution:

Which statement describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?

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

正解：A

解説：
When using registered_model_name=model_name in mlflow.spark.log_model(), MLflow automatically registers the logged model under the specified model name. If that model name already exists in the MLflow Model Registry, MLflow creates a new version of that existing registered model rather than a new model entry. This enables automatic versioning and continuous model refresh with each pipeline run.

質問 # 134
What does an Estimator do in Spark ML?

A. Evaluates model performance
B. Stores features
C. Transforms a dataset
D. Trains a model using data

正解：D

解説：
An Estimator learns from data and produces a Transformer.

質問 # 135
Which of the following describes label drift?

A. Label drift is when there is a change in the distribution of an input variable
B. Label drift is when there is a change in the distribution of the predicted target given by the model
C. None of these describe label drift
D. Label drift is when there is a change in the distribution of a target variable
E. Label drift is when there is a change in the relationship between input variables and target variables

正解：A

質問 # 136
Which MLflow function logs files like plots or datasets?

A. mlflow.log_artifact()
B. mlflow.log_param()
C. mlflow.save_model()
D. mlflow.log_metric()

正解：A

解説：
Artifacts include:
charts
model files
datasets.

質問 # 137
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

A. Edge/on-device
B. Streaming
C. Real-time
D. Batch

正解：C

解説：
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.

質問 # 138
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