시험준비에가장좋은Databricks-Machine-Learning-Professional퍼펙트인증공부자료최신버전덤프자료

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Model Deployment | 10% | - Model Serving
- 1. Custom serving solutions
- 2. Databricks Model Serving
- Deployment Strategies
- 1. Real-time Deployment
- 2. Streaming Deployment
- 3. Batch Deployment
|
| Topic 2: Model Development | 47% | - Feature Store
- 1. Automated feature pipelines
- 2. Feature Store concepts and usage
- MLflow Advanced Features
- 1. Experiment tracking and model management
- 2. Model registry and versioning
- SparkML and Scaling
- 1. Distributed training and hyperparameter tuning
- 2. Ray/Optuna distributed tuning
- 3. Building scalable ML pipelines with SparkML
|
| Topic 3: Model Lifecycle Management (MLOps) | 43% | - Testing Strategies
- 1. Model testing and validation
- 2. Data quality testing
- Monitoring
- 1. Model performance monitoring
- 2. Lakehouse Monitoring for drift detection
- Automated Workflows
- 1. CI/CD pipelines for ML
- 2. Automated retraining workflows
- Environment Management
- 1. Environment configuration and reproducibility
- 2. Databricks Asset Bundles (DABs)
|
>> Databricks-Machine-Learning-Professional퍼펙트 인증공부자료 <<
실제 Databricks-Machine-Learning-Professional 시험덤프자료, Databricks-Machine-Learning-Professional 시험대비공부, 최신 Databricks-Machine-Learning-Professional 덤프자료
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최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q23-Q28):
질문 # 23
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?
- A.

- B. client.qet_run(exp_id.run_id).data.metrics
- C. client.get_run(run_id).data.metrics
- D. spark.read.format("mlflow-run").load(run_id)
정답:C
설명:
The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(...)) is for querying across multiple runs, not for accessing a specific run's metrics.
질문 # 24
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?
- A. "MODEL_VERSION_TRANSITIONED_STAGE"
- B. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- C. "MODEL_VERSION_CREATED"
- D. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- E. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
정답:E
질문 # 25
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. None of these use cases require the use of an HTTP Webhook
- B. Starting a testing job when a new model is registered
- C. Sending a message to a Slack channel when a model version transitions stages
- D. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- E. Sending an email alert when an automated testing Job fails
정답:D
질문 # 26
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?
- A. mlflow.models.Model.signature
- B. mlflow.models.schema.infer_schema
- C. mlflow.models.Model.get_input_schema
- D. There is no way to obtain the input schema and the output schema of an unlogged model.
- E. mlflow.models.signature.infer_signature
정답:B
질문 # 27
Which of the following describes concept drift?
- A. None of these describe Concept drift
- B. Concept drift is when there is a change in the distribution of an input variable
- C. Concept drift is when there is a change in the distribution of a target variable
- D. Concept drift is when there is a change in the relationship between input variables and target variables
- E. Concept drift is when there is a change in the distribution of the predicted target given by the model
정답:E
질문 # 28
......
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