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

TopicDetails
Topic 1
  • 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 2
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3
  • 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 4
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 5
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 6
  • 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 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 that data can arrive out-of-order with structured streaming
  • Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

Answer: B

Explanation:
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.


NEW QUESTION # 26
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

Answer: A


NEW QUESTION # 27
Label drift occurs where there is a change in which element?

Answer: D

Explanation:
Label drift refers to a change in the distribution of the target variable over time. This means the frequencies or proportions of classes or target values shift, which can impact model performance even if the input feature distributions remain unchanged.


NEW QUESTION # 28
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

Answer: C


NEW QUESTION # 29
Why is Delta Lake time travel useful in ML pipelines?

Answer: B

Explanation:
Time travel allows training on exact historical datasets.


NEW QUESTION # 30
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