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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Section	Weight	Objectives
ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Automated retraining workflows - Testing and validation strategies - Environment management with Databricks Asset Bundles
Model Development	44%	- Feature Store and automated feature pipelines - Scalable ML pipelines with SparkML - Advanced MLflow usage - Distributed training and hyperparameter tuning
Model Deployment	12%	- Model rollout and version management - Custom model serving - Deployment strategies

>> Databricks-Machine-Learning-Professional資格問題集 <<

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q113-Q118):

質問 # 113
A machine learning engineer needs to deliver predictions of a machine learning model in real- time. However, the feature values needed for computing the predictions are available one week before the query time. Which feature is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Batch serving has built-in capabilities in Databricks Machine Learning
B. There is no advantage to using batch serving deployments over real-time serving deployments
C. Testing is not possible in real-time serving deployments
D. Querying stored predictions can be faster than computing predictions in real-time
E. Computing predictions in real-time provides more up-to-date results

正解：A

質問 # 114
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

正解：D

質問 # 115
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables. Which of the following tools can the machine learning engineer use to assess their theory?

A. None of these
B. One-way Chi-squared Test
C. Jenson-Shannon distance
D. Two-way Chi-squared Test
E. Kolmogorov-Smirnov (KS) test

正解：B

質問 # 116
A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?

A. Create a derived metric for the composite_risk_rating calculation and create a drift metric on this derived metric.
B. Create an aggregate metric for composite_risk_rating and configure a separate drift metric to monitor changes.
C. Use a scheduled notebook to calculate the composite score and manually insert both the score and drift values into the profile metrics table.
D. Create two separate aggregate metrics: one for the composite score calculation and another for drift detection.

正解：A

解説：
Lakehouse Monitoring supports derived metrics that are computed from existing profile metrics using custom expressions. By defining a derived metric for the composite_risk_rating using the specified weights, the composite score becomes a first-class metric in the monitoring framework.
A drift metric can then be directly configured on this derived metric to compare current values against the baseline, fulfilling both the composite calculation and drift monitoring requirements in a native, governed way.

質問 # 117
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 of the following lines of code can they use to register the model to the MLflow Model Registry?

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

正解：C

質問 # 118
......

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