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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Available Languages:	English
Exam Price:	USD 200
Certificate Validity Period:	2 years
Passing Score:	70%
Real Exam Qty:	60
Exam Format:	Multiple choice
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	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
Thema 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 3	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 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Thema 5	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 6	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
Thema 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q56-Q61):

56. Frage
A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns:

However, instances of this class are unable to compute predictions.
Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?

A. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the preprocess_input method
B. Replace self.rf_model.predict(input) with self.predict(input) in the predict method
C. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the predict method
D. Remove the self.rf_model = rf_model line from the fit method

Antwort: C

Begründung:
The issue is that the predict() method does not apply the same preprocessing as the fit() method.
During training, the model uses preprocessed data (via self.preprocess_input()), but during prediction, it directly uses raw input. This mismatch causes prediction errors because the model expects preprocessed input.
By updating the predict() method to call self.preprocess_input(model_input.copy()), both training and prediction use consistent feature transformations, allowing predictions to be computed successfully.

57. Frage
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

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

Antwort: B

58. Frage
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. The pvfunc model can be used to deploy models in a parallelizable fashion
B. The same preprocessing logic will automatically be applied when calling fit
C. The same preprocessing logic will automatically be applied when calling predict
D. There is no longer a need for pipeline-like machine learning objects
E. This approach has no impact when loading the logged Pvfunc model for downstream deployment

Antwort: D

59. Frage
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?

A. mlflow.sklearn.load_model(model_uri)
B. mlflow.sklearn.read_model(model_uri)
C. mlflow.pyfunc.read_model(model_uri)
D. mlflow.pyfunc.load_model(model_uri)
E. mlflow.spark.load_model(model_uri)

Antwort: D

60. Frage
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Obtain the observed values (actual) feature values
B. Retrain the model
C. Compute the evaluation metric using the observed and predicted values
D. Measure the latency of the prediction time
E. None of these should be completed as Step #3

Antwort: E

61. Frage
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