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

TopicDetails
Topic 1
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 2
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4
  • 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 5
  • 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 6
  • 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 7
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 8
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9
  • 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 10
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 11
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

Answer: E


NEW QUESTION # 35
Which tool can be used to automatically start a testing Job when a new version of an MLflow Model Registry model is registered?

Answer: B

Explanation:
MLflow Model Registry Webhooks can be configured to automatically trigger actions - such as running a testing job - when events occur, like registering a new model version. This enables automation in CI/CD workflows for machine learning models.


NEW QUESTION # 36
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: A


NEW QUESTION # 37
A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?

Answer: C

Explanation:
The Pandas Function API supports parallel, group-specific processing by using groupBy on the grouping column and applyInPandas to execute a custom training function independently for each group. This enables separate models to be trained per region in parallel across the cluster, with each function invocation receiving only the data for its group.


NEW QUESTION # 38
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?

Answer: C


NEW QUESTION # 39
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