Databricks-Machine-Learning-Professional Fragen & Antworten & Databricks-Machine-Learning-Professional Studienführer & Databricks-Machine-Learning-Professional Prüfungsvorbereitung

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Thema 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Thema 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 5	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
Thema 6	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Thema 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 8	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Thema 9	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

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q82-Q87):

82. Frage
Which of the following is a probable response to identifying drift in a machine learning application?

A. Sunsetting the machine learning application
B. All of these responses
C. Retraining and deploying a model on more recent data
D. None of these responses
E. Rebuilding the machine learning application with a new label variable

Antwort: D

83. Frage
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?

A. Production
B. Archived
C. None
D. Staging

Antwort: A

Begründung:
In MLflow Model Registry stages:
Staging -> testing before release
Production -> serving real users
Archived -> retired models

84. Frage
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_metric
B. mlflow.log_model
C. There is no way to store values like this.
D. mlflow.log_artifact
E. mlflow.log_param

Antwort: A

85. Frage
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?

A. Obtain the observed values (actual) feature values and compare to the predicted values
B. Measure the latency of the model's prediction time
C. Retrain the model based on any new feature variables that have been added to the feature set
D. Run a statistical test to determine if there are changes in the feature variable distribution over time

Antwort: D

Begründung:
The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.

86. Frage
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. The new model must perform better than the original model on a random subset of the available data
B. The new model must perform better than the original model on the most recently available data
C. All of these statements must be true prior to deploying the new model
D. The now model must perform better than the original model on all of the available data

Antwort: B

Begründung:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

87. Frage
......

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