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

TopicDetails
Topic 1
  • Identify a use case for HTTP webhooks and where the Webhook URL needs to come
  • Identify advantages of using Job clusters over all-purpose clusters
Topic 2
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 3
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 4
  • 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
Topic 5
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 6
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q129-Q134):

NEW QUESTION # 129
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?

Answer: C


NEW QUESTION # 130
Which of the following statements about built-in library-specific MLflow Model flavors is true?

Answer: C

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.


NEW QUESTION # 131
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

Answer: A


NEW QUESTION # 132
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?

Answer: D


NEW QUESTION # 133
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

Answer: A

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


NEW QUESTION # 134
......

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