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Databricks-Machine-Learning-Professional Exam Questions
- Topic 1: Identify the requirements for tracking nested runs/ Describe an MLflow flavor and the benefits of using MLflow flavors
- Topic 2: 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 3: 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 4: 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 5: Describe the advantages of using the pyfunc MLflow flavor/ Manually log parameters, models, and evaluation metrics using MLflow
- Topic 6: Identify live serving benefits of querying precomputed batch predictions/ Describe Structured Streaming as a common processing tool for ETL pipelines
- 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 which code block will trigger a shown webhook/ Describe the basic purpose and user interactions with Model Registry
- Topic 9: Identify less performant data storage as a solution for other use cases/ Describe why complex business logic must be handled in streaming deployments
- Topic 10: 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 11: Identify JIT feature values as a need for real-time deployment/ Describe how to list all webhooks and how to delete a webhook
- Topic 12: Describe model serving deploys and endpoint for every stage/ Identify scenarios in which feature drift and/or label drift are likely to occur
Free Databricks Databricks-Machine-Learning-Professional Exam Actual Questions
Note: Premium Questions for Databricks-Machine-Learning-Professional were last updated On Nov. 18, 2024 (see below)
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?
A)
B)
C)
D)
E)
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:
The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?
A)
B)
C)
features_df.write.mode("fs").path("new_table")
D)
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?
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