Databricks-Machine-Learning-Professional Prüfungs, Databricks-Machine-Learning-Professional Dumps Deutsch

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Thema 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 4	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 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 6	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 7	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 Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q68-Q73):

68. Frage
A Data Scientist is using Spark ML to train a model for detecting fraudulent transactions (1:
fraudulent, 0: non-fraudulent). To maximize the model's overall AUC (ROC) score, the Data Scientist wants to perform hyperparameter tuning on a Random Forest classifier. Due to budget constraints, the tuning process must be completed efficiently to avoid excessive compute costs.
Which approach will fulfill their needs?

Antwort: C

Begründung:
TrainValidationSplit evaluates each hyperparameter combination using a single train/validation split, which is significantly more compute-efficient than k-fold cross-validation. Using a BinaryClassificationEvaluator aligns directly with optimizing AUC (ROC) for a binary fraud detection task, making this approach well suited for maximizing model performance under budget and compute constraints.

69. Frage
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?

A.
B.
C. mlflow.sklearn.log_model(model, "model")
mlflow.log_signature(model_signature)
mlflow.log_input(input_records)
D. There is no way to log a model signature.

Antwort: A

Begründung:
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.

70. Frage
A machine learning engineer wants to load the data from the very first version of a Delta table from location path. Which of the following lines of code can be used to accomplish this task?

A. spark.read.format("delta").option("versionAsOf", 0).load(path)
B. spark.read.format("delta").option("version", 1).load(path)
C. spark.read.format("delta").option("version", 0).load(path)
D. spark.read.format("delta").option("versionAsOf", 1).load(path)

Antwort: D

Begründung:
In Delta Lake, the option("versionAsOf", <version_number>) parameter is used to load a specific version of a Delta table.
Since Delta table versions are 1-indexed (the first version is version 1), using .option("versionAsOf", 1) correctly loads the very first version of the table.

71. Frage
How can you save a trained Spark ML PipelineModel?

A. pipeline.persist()
B. pipeline.store()
C. pipeline.save()
D. pipelineModel.write().save()

Antwort: D

Begründung:
Example:
pipelineModel.write().overwrite().save("/model")

72. Frage
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Which of the following deployment strategies can be used to meet these requirements?

A. Streaming
B. Real-time
C. Batch
D. Edge/on-device
E. None of these strategies will meet the requirements.

Antwort: B

73. Frage
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