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Databricks Databricks-Machine-Learning-Professional 시험요강:

주제	소개
주제 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
주제 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
주제 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
주제 4	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
주제 5	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
주제 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
주제 7	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
주제 8	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
주제 9	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
주제 10	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
주제 11	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

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최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q100-Q105):

질문 # 100
A Machine Learning Engineer needs to develop a custom anomaly detection model that monitors the internal IT infrastructure of their company. The model takes in compute metrics, logs, and user data and generates a binary prediction. The engineer plans to deploy it as a Databricks Model Serving endpoint. In production there will only be one client calling the endpoint once every
15 seconds. Leadership sees the model as an important part of their operational improvement strategy so maintaining consistent, stable, low latency inference is a requirement while minimizing infrastructure costs. The engineer plans to deploy the endpoint via the MLflow Deployment SDK.
Which endpoint config for the MLflow Deployment SDK should the engineer select?

정답：C

설명：
With a single client making requests every 15 seconds, the traffic is steady and predictable, and low-latency inference is a strict requirement. Disabling scale-to-zero avoids cold start latency, ensuring consistent response times. Selecting a Small workload size minimizes infrastructure costs while still providing sufficient resources for a lightweight binary classification model, making this configuration the best balance between performance, stability, and cost.

질문 # 101
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Development. Staging. Production. Archived
B. Development. Staging. Production
C. Staging. Production. Archived
D. None. Staging. Production. Archived
E. None. Staging. Production

정답：B

질문 # 102
A team is building a machine learning pipeline, which includes feature engineering, model training, model evaluation, and model deployment. Over the last year, the project has grown a lot, with multiple contributors working on it. Currently, they are manually testing their pipelines and functions. However, they are looking to build out a MLOps process and want to organize their unit tests. Which approach to organizing functions and unit tests should they take?

A. Place all functions and their corresponding unit tests in a single notebook or script.
B. Separate functions into dedicated modules and place all unit tests in a separate test suite or notebook.
C. Organize functions and unit tests together in multiple notebooks, grouping them by feature or component.
D. Write unit tests only after deployment to avoid slowing down development.

정답：B

설명：
Separating production code into dedicated modules and organizing unit tests in a separate test suite follows standard software engineering and MLOps best practices. This structure improves maintainability, enables automated testing in CI/CD pipelines, supports collaboration among multiple contributors, and scales effectively as the project grows.

질문 # 103
A Data Scientist is developing a model training pipeline on Databricks and needs to track custom performance metrics during training. They want to log a custom evaluation score (team_score), a single hyperparameter, and a confusion matrix plot as part of their MLflow experiment. Which code snippet correctly logs all three types of information in MLflow?

A. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact("confusion_matrix.png")
B. mlflow.log_metric({"team_score": 0.92})
mlflow.log_param(["max_depth", 5])
mlflow.log_file("confusion_matrix.png")
C. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact(open("confusion_matrix.png"))
D. mlflow.log_param("max_depth", "5")
mlflow.log_metric("team_score", "0.92")
mlflow.log_artifact("confusion_matrix.png")

정답：A

설명：
This snippet correctly uses MLflow's APIs to log each item in its expected form: a single hyperparameter with log_param, a numeric custom metric with log_metric, and a file-based artifact such as a confusion matrix image by passing its file path to log_artifact. This is the standard and correct way to track parameters, metrics, and artifacts in an MLflow experiment.

질문 # 104
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. Real-time
B. Edge/on-device
C. Batch
D. Streaming

정답：A

설명：
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.

질문 # 105
......

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Databricks-Machine-Learning-Professional유효한덤프, Databricks-Machine-Learning-Professional최신시험최신덤프자료

Databricks Databricks-Machine-Learning-Professional 시험요강:

최신 Databricks-Machine-Learning-Professional유효한 덤프 인증시험 인기 덤프문제 다운

최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q100-Q105):