Databricks-Machine-Learning-Professional최신업데이트덤프공부최신인기시험자료

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

Section	Weight	Objectives
ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies - Automated retraining workflows - Environment management with Databricks Asset Bundles
Model Deployment	12%	- Custom model serving - Deployment strategies - Model rollout and version management
Model Development	44%	- Distributed training and hyperparameter tuning - Advanced MLflow usage - Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines

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Databricks-Machine-Learning-Professional최신 업데이트 덤프공부 덤프는 Databricks Certified Machine Learning Professional 시험을 단번에 패스하는 필수자료

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최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q153-Q158):

질문 # 153
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?

정답：C

설명：
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.

질문 # 154
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

A. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.
B. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
Use groupby.applyInPandas.
C. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
D. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.

정답：D

설명：
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

질문 # 155
Which of the following describes label drift?

A. Label drift is when there is a change in the relationship between input variables and target variables
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 distribution of an input variable

정답：E

질문 # 156
How can you save a trained Spark ML PipelineModel?

A. pipeline.persist()
B. pipeline.save()
C. pipeline.store()
D. pipelineModel.write().save()

정답：D

설명：
Example:
pipelineModel.write().overwrite().save("/model")

질문 # 157
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

A. TIMESTAMP
B. HISTORY
C. DESCRIBE HISTORY
D. DESCRIBE
E. VERSION

정답：C

질문 # 158
......

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