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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
  • 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 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
  • 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 5
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q127-Q132):

NEW QUESTION # 127
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

Answer: C

Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.


NEW QUESTION # 128
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They have created a model signature model_signature and a few example input records input_records. Which block of code can be used to log all of this information?

Answer: D

Explanation:
The code block correctly uses the mlflow.sklearn.log_model() function with both signature and input_example parameters.
signature=model_signature defines the expected model input and output schema, ensuring reproducibility and validation during inference.
input_example=input_records provides a sample input that helps users understand the model's expected input format.


NEW QUESTION # 129
Which MLflow feature helps reproduce training runs?

Answer: D

Explanation:
Tracking logs:
parameters
code version
metrics
artifacts
making runs reproducible.


NEW QUESTION # 130
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?

Answer: C


NEW QUESTION # 131
A machine learning engineer would like to compute predictions on inference data as it becomes available through the pipeline in microbatches. The predictions should be stored in a table for query later. Which deployment strategy can the engineer use?

Answer: C

Explanation:
A streaming deployment strategy processes incoming data continuously or in microbatches, allowing predictions to be computed as new data arrives. The results can then be written to a table for later querying. This approach balances near-real-time inference with manageable resource use, making it ideal for scenarios where data flows steadily through a pipeline.


NEW QUESTION # 132
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