최신 Databricks-Machine-Learning-Professional 무료덤프 - Databricks Certified Machine Learning Professional

문제1

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. Streaming

E. None of these strategies will meet the requirements.

정답: A

문제2

Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Experiments

B. Model Registry

C. Feature Store

D. Model Serving

E. Models

정답: D

문제3

Which of the following describes the concept of MLflow Model flavors?

A. A convention that MLflow Model Registry can use to version models

B. A convention that MLflow Experiments can use to organize their Runs by project

C. A convention that MLflow Model Registry can use to organize its Models by project

D. A convention that deployment tools can use to understand the model

E. A convention that deployment tools can use to wrap preprocessing logic into a Model

정답: B

문제4

A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?

A. [Structured Streaming

B. AutoML

C. Spark UDFs

D. MLflow
D Delta Lake

정답: C

문제5

Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.write_table

B. fs.create_table

C. There is no way to accomplish this task with fs

D. fs.get_table

E. fs.read_table

정답: B

문제6

A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Testing is not possible in real-time serving deployments

B. There is no advantage to using batch serving deployments over real-time serving deployments

C. Batch serving has built-in capabilities in Databricks Machine Learning

D. Computing predictions in real-time provides more up-to-date results

E. Querying stored predictions can be faster than computing predictions in real-time

정답: C

최신 Databricks-Machine-Learning-Professional 무료덤프 - Databricks Certified Machine Learning Professional

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