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

SectionWeightObjectives
Model Deployment12%- Model rollout and version management
- Custom model serving
- Deployment strategies
Model Development44%- Scalable ML pipelines with SparkML
- Feature Store and automated feature pipelines
- Advanced MLflow usage
- Distributed training and hyperparameter tuning
ML Ops44%- Model monitoring and drift detection with Lakehouse Monitoring
- Automated retraining workflows
- Testing and validation strategies
- Environment management with Databricks Asset Bundles

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Databricks Certified Machine Learning Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
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?

Answer: B


NEW QUESTION # 50
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?

Answer: A


NEW QUESTION # 51
A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?

Answer: A

Explanation:
Spark ML algorithms, including gradient-boosted trees, are designed around data parallelism. By increasing the number of executor nodes, the dataset can be further partitioned so each executor processes only a subset of the data, reducing per-executor memory pressure while keeping the same model configuration and dataset size. This leverages Spark's distributed architecture without requiring model parallelism or hyperparameter changes.


NEW QUESTION # 52
Which evaluator is used for binary classification?

Answer: B

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.


NEW QUESTION # 53
A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.
Which of the following model URIs can be used to query the described model version?

Answer: E


NEW QUESTION # 54
......

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