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| Topic | Details |
|---|
| Topic 1 | - 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 2 | - Identify which code block will trigger a shown webhook
- Describe the basic purpose and user interactions with Model Registry
|
| Topic 3 | - Identify the requirements for tracking nested runs
- Describe an MLflow flavor and the benefits of using MLflow flavors
|
| 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
|
| Topic 7 | - Identify less performant data storage as a solution for other use cases
- Describe why complex business logic must be handled in streaming deployments
|
| Topic 8 | - Describe the advantages of using the pyfunc MLflow flavor
- Manually log parameters, models, and evaluation metrics using MLflow
|
| Topic 9 | - Describe model serving deploys and endpoint for every stage
- Identify scenarios in which feature drift and
- or label drift are likely to occur
|
| Topic 10 | - 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 11 | - Identify JIT feature values as a need for real-time deployment
- Describe how to list all webhooks and how to delete a webhook
|
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Databricks Certified Machine Learning Professional Sample Questions (Q76-Q81):
NEW QUESTION # 76
Which is a benefit of logging an input example with an MLflow model?
- A. The environment requirements are packaged with the MLmodel file
- B. An example prediction is available to test post-inference pipelines
- C. The model evaluation metrics are stored with the model
- D. An example record is available to test serving applications
Answer: D
Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.
NEW QUESTION # 77
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?
Answer: A
Explanation:
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.
NEW QUESTION # 78
Which of the following describes label drift?
- A. None of these describe label drift
- B. Label drift is when there is a change in the relationship between input variables and target variables
- C. Label drift is when there is a change in the distribution of the predicted target given by the model
- D. Label drift is when there is a change in the distribution of a target variable
- E. Label drift is when there is a change in the distribution of an input variable
Answer: E
NEW QUESTION # 79
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which lines of code can be used to fill in the blank so the code block can successfully complete the task?
- A. mlflow.spark.log_model(sklearn_model, "model")
- B. mlflow.spark.track_model(sklearn_model, "model")
- C. mlflow.sklearn.load_model("model")
- D. mlflow.sklearn.log_model(sklearn_model, "model")
- E. mlflow.sklearn.track_model(sklearn_model, "model")
Answer: B
NEW QUESTION # 80
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?
- A. JS does not require any manual threshold or cutoff determinations
- B. All of these reasons
- C. JS is not normalized or smoothed
- D. None of these reasons
- E. JS is more robust when working with large datasets
Answer: E
NEW QUESTION # 81
......
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