Databricks-Machine-Learning-Professional시험대비덤프최신샘플덤프로시험에도전

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Databricks Databricks-Machine-Learning-Professional 시험요강:

주제	소개
주제 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
주제 2	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
주제 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
주제 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

>> Databricks-Machine-Learning-Professional시험대비 덤프 최신 샘플 <<

Databricks-Machine-Learning-Professional시험대비 덤프 최신 샘플 덤프자료

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최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q72-Q77):

질문 # 72
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Which of the following deployment strategies can be used to meet these requirements?

A. Real-time
B. Batch
C. Edge/on-device
D. None of these strategies will meet the requirements.
E. Streaming

정답：A

질문 # 73
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

A. Chi-squared test
B. Kolmogorov-Smirnov (KS) test
C. Summary statistics trends
D. None of these can be used to monitor feature drift
E. Jensen-Shannon test

정답：C

질문 # 74
A Machine Learning Engineer has previously built a feature table for model training and inference using a batch mode approach:

They have been informed that they now require these features to be available in "real-time", with latency on the order of a minute. Their manager has informed them there is now a Kafka stream from which they can stream live data, and they need to have this ingested and available for low- latency feature lookups.
Which change to their existing code will achieve this?

A. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
B. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
C. Change the incoming_df to be a dataframe based on a readStream() from the Kafka source and publish the table as an online table with the streaming option set to True.
D. Change the incoming_df to be a dataframe based on a readStream() from the kafka source, the write_table() method will provide a low-latency lookup on this data.

정답：C

설명：
To achieve real-time availability with minute-level latency, the feature data must be continuously ingested from Kafka and published to an online table. Using a streaming DataFrame created with readStream from the Kafka source and enabling the online table with streaming allows incremental updates to be synchronized to the online store, supporting low-latency feature lookups for real-time inference.

질문 # 75
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function can be used to store models in an MLmodel file
B. python_function can be used to deploy models in a parallelizable fashion
C. python_function provides no benefits over the built-in library-specific model flavors
D. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
E. python_function can be used to deploy models without worrying about which library was used to create the model

정답：B

질문 # 76
What is the main purpose of the Databricks Feature Store?

A. Storing trained models
B. Centralizing reusable ML features
C. Replacing Spark ML pipelines
D. Storing raw data

정답：B

설명：
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

질문 # 77
......

Databricks Databricks-Machine-Learning-Professional인증시험에 응시하고 싶으시다면 좋은 학습자료와 학습 가이드가 필요합니다.Databricks Databricks-Machine-Learning-Professional시험은 it업계에서도 아주 중요한 인증입니다. 시험패스를 원하신다면 충분한 시험준비는 필수입니다.

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