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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q186-Q191):

NEW QUESTION # 186
A data scientist has developed and logged a Spark ML random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the featureImportances of the original model object. Which lines of code can be used to restore the model object so that featureImportances is available?

A. client.list_artifacts(run_id)["feature-importances.csv"]
B. client.pyfunc.load_model(model_uri)
C. mlflow.load_model(model_uri)
D. This can only be viewed in the MLflow Experiments UI
E. mlflow.sklearn.load_model(model_uri)

Answer: E

NEW QUESTION # 187
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?

A. Databricks REST APIs
B. Jobs cannot be created programmatically
C. MLflow APIs
D. MLflow Client
E. AutoML APIs

Answer: A

NEW QUESTION # 188
What is the main purpose of the Databricks Feature Store?

A. Centralizing reusable ML features
B. Replacing Spark ML pipelines
C. Storing trained models
D. Storing raw data

Answer: A

Explanation:
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

NEW QUESTION # 189
What does an Estimator do in Spark ML?

A. Trains a model using data
B. Transforms a dataset
C. Evaluates model performance
D. Stores features

Answer: A

Explanation:
An Estimator learns from data and produces a Transformer.

NEW QUESTION # 190
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?

Answer: D

Explanation:
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.

NEW QUESTION # 191
......

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