최신 Databricks-Machine-Learning-Professional 무료덤프 - Databricks Certified Machine Learning Professional
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?
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?
정답: A
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A machine learning engineer would like to compute predictions on inference data as it becomes available through the pipeline in microbatches. The predictions should be stored in a table for query later. Which deployment strategy can the engineer use?
정답: D
설명: (DumpTOP 회원만 볼 수 있음)
Which stage in the MLflow Model Registry is typically used for models currently serving production traffic?
정답: B
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After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?
정답: C
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?
정답: B
What is the main purpose of the Databricks Feature Store?
정답: A
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A Data Scientist is training a complex gradient-boosted model for fraud detection. The model uses dynamic threshold tuning during training and generates custom visualizations of feature drift. To ensure reproducibility and collaboration, they need to programmatically track:
- Custom metrics (e g., adjusted_f1 for threshold variations)
- Hyperparameters from nested configuration files
- Drift visualization plots as PDFs
Which approach implements this tracking in MLflow?
- Custom metrics (e g., adjusted_f1 for threshold variations)
- Hyperparameters from nested configuration files
- Drift visualization plots as PDFs
Which approach implements this tracking in MLflow?
정답: C
설명: (DumpTOP 회원만 볼 수 있음)
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
정답: B
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?
정답: D
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Which of the following describes concept drift?
정답: D
A Data Scientist at a company with rapidly increasing sales has deployed a scikit-learn model in production, which is retrained weekly on a single-node cluster. During the most recent retraining, the job failed due to an out-of-memory error. Upon investigation, the Data Scientist discovered that the training data had increased to 700GB as a result of the company's expanding customer base. Which approach will reliably resolve this issue in the long term?
정답: B
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