Databricks-Machine-Learning-Professional Deutsche Prüfungsfragen, Databricks-Machine-Learning-Professional Quizfragen Und Antworten

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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Thema 4	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 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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Thema 7	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 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 9	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 10	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 11	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

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q57-Q62):

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

Antwort: D

58. Frage
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
B. mlflow.register_model(run_id, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/model")
D. mlflow.register_model(model_uri, "best_model")
E. mlflow.register_model(model_uri, "model")

Antwort: D

59. 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.

60. Frage
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files. Based on the scenario, which optimization technique could speed up the query by colocating similar records while considering values in multiple columns?

A. Write as a Parquet file
B. Data skipping
C. Bin-packing
D. Tuning the file size
E. Z-Ordering

Antwort: E

61. Frage
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Model Serving
B. Experiments
C. Feature Store
D. Models
E. Model Registry

Antwort: E

62. Frage
......

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