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Reference: https://www.databricks.com/learn/certification/machine-learning-professional
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Databricks Certified Machine Learning Professional Sample Questions:
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A. Jensen-Shannon test
- B. None of these can be used to monitor feature drift
- C. Summary statistics trends
- D. Chi-squared test
- E. Kolmogorov-Smirnov (KS) test
Correct Answer: C 🗳️
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?
- A. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the preprocess_input method
- B. Replace model_input.copy() with self.preprocess_input(model_input.copy()) in the predict method
- C. Replace self.rf_model.predict(input) with self.predict(input) in the predict method
- D. Remove the self.rf_model = rf_model line from the fit method
Correct Answer: B 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?
- A. Retrain the model based on any new feature variables that have been added to the feature set
- B. Obtain the observed values (actual) feature values and compare to the predicted values
- C. Measure the latency of the model's prediction time
- D. Run a statistical test to determine if there are changes in the feature variable distribution over time
Correct Answer: D 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
A Machine Learning Engineer is building a fraud detection model that needs to use both pre- computed features from a feature table and real-time calculated features based on user location data sent with each inference request. The engineer has created a Python UDF called calculate_distance in Unity Catalog at main.fraud_detection.calculate_distance that computes the distance between a transaction location and the user's current location. The feature table main.fraud_detection.user_features contains historical user spending patterns with primary key user_id.
The engineer has written the following code to implement this scenario:
Which benefit of this implementation approach makes it suited to the real-time fraud detection use case?
- A. The model automatically performs feature lookup and computation during inference without additional serving code.
- B. The FeatureLookup function avoids the need for joining the full table main.fraud_detection.user_features with training_set increasing efficiency.
- C. The Unity Catalog registry automatically creates REST API endpoints for UDF functions used in feature computation.
- D. The FeatureFunction caches computed distance values in the online store to improve inference latency for location pairs.
Correct Answer: A 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
Which of the following statements about built-in library-specific MLflow Model flavors is true?
- A. Built-in library-specific flavors can only be used for logging models
- B. Built-in library-specific flavors allow models to be used with any library/span>
- C. Built-in library-specific flavors allow models to be exported as library objects
- D. Built-in library-specific flavors are required for model signature use
Correct Answer: C 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
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