Databricks Databricks-Machine-Learning-Professional Fragen und Antworten, Databricks Certified Machine Learning Professional Prüfungsfragen

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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
Passing Score:	Not publicly disclosed
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Real Exam Qty:	Approximately 45–60
Available Languages:	English
Exam Format:	Multiple choice, Scenario-based questions, Multiple select
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Thema 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 4	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 5	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q67-Q72):

67. Frage
A data scientist set up a machine learning pipeline to automatically log feature importance data from a CSV with each run. They now want to view the feature importance data in Databricks.
Which location in Databricks will show this data?

A. The Artifacts section of the MLflow Experiment page
B. The Data section of the MLflow Run page/span>
C. The Artifacts section of the MLflow Run page
D. The MLflow Model Registry Model page

Antwort: C

Begründung:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.

68. Frage
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.
Which of the following code blocks can they use to accomplish the task?

Antwort: D

69. Frage
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.
B. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
C. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.
D. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.

Antwort: D

Begründung:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

70. Frage
Which Spark ML class supports automated hyperparameter tuning?

A. ParamGridBuilder
B. BinaryClassificationEvaluator
C. CrossValidator
D. PipelineModel

Antwort: C

Begründung:
CrossValidator performs:
parameter search
k-fold cross validation.

71. Frage
Which MLflow feature helps reproduce training runs?

A. MLflow Tracking
B. MLflow Projects
C. Model Serving
D. Delta Sharing

Antwort: A

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

72. Frage
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