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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 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

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Databricks Certified Machine Learning Professional Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which of the following is a probable response to identifying drift in a machine learning application?

A. Rebuilding the machine learning application with a new label variable
B. Retraining and deploying a model on more recent data
C. Sunsetting the machine learning application
D. None of these responses
E. All of these responses

Answer: D

NEW QUESTION # 56
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

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

Answer: C

Explanation:
In MLflow Model Registry stages:
Staging -> testing before release
Production -> serving real users
Archived -> retired models

NEW QUESTION # 57
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. The same preprocessing logic will automatically be applied when calling fit
B. There is no longer a need for pipeline-like machine learning objects
C. The pvfunc model can be used to deploy models in a parallelizable fashion
D. This approach has no impact when loading the logged Pvfunc model for downstream deployment
E. The same preprocessing logic will automatically be applied when calling predict

Answer: B

NEW QUESTION # 58
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

A. The Artifacts section of the MLflow Run page
B. The MLflow Model Registry Model paqe
C. Logged data visualizations cannot be viewed in Databricks
D. The Artifacts section of the MLflow Experiment page
E. The Figures section of the MLflow Run page

Answer: E

NEW QUESTION # 59
What does an Estimator do in Spark ML?

A. Evaluates model performance
B. Transforms a dataset
C. Trains a model using data
D. Stores features

Answer: C

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 60
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