How to Prepare for Databricks-Machine-Learning-Professional Certification Exam?

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

TopicDetails
Topic 1
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 2
  • Create, overwrite, merge, and read Feature Store tables in machine learning workflows
  • View Delta table history and load a previous version of a Delta table
Topic 3
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
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
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 6
  • 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 7
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9
  • 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

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Databricks Certified Machine Learning Professional Sample Questions (Q69-Q74):

NEW QUESTION # 69
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They notice that not all details and objects are automatically logged, and they will need to manually log some things. Which of the following will need to be manually logged when performing nested runs with Hyperopt and MLflow Autologging?

Answer: E

Explanation:
When using MLflow Autologging with Hyperopt and nested runs, the best trial evaluation metric is not automatically logged and must be logged manually. Autologging captures trial-level details like hyperparameters and evaluation metrics, but summarizing and logging the overall best trial's result is a manual responsibility of the engineer.


NEW QUESTION # 70
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

Answer: B


NEW QUESTION # 71
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: D


NEW QUESTION # 72
A data scientist has developed and logged a Spark ML random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the featureImportances of the original model object. Which lines of code can be used to restore the model object so that featureImportances is available?

Answer: B


NEW QUESTION # 73
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

Answer: B


NEW QUESTION # 74
......

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