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

SectionWeightObjectives
Topic 1: ML Ops44%- Model monitoring and drift detection with Lakehouse Monitoring
- Environment management with Databricks Asset Bundles
- Testing and validation strategies
- Automated retraining workflows
Topic 2: Model Deployment12%- Model rollout and version management
- Deployment strategies
- Custom model serving
Topic 3: Model Development44%- Feature Store and automated feature pipelines
- Distributed training and hyperparameter tuning
- Scalable ML pipelines with SparkML
- Advanced MLflow usage

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Databricks Certified Machine Learning Professional Sample Questions (Q82-Q87):

NEW QUESTION # 82
A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

Answer: D

Explanation:
MinMaxScaler rescales each numeric feature to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the feature's range. This makes it the appropriate Spark ML transformer when an algorithm explicitly requires inputs normalized to the [0,1] interval.


NEW QUESTION # 83
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 URls can be used to query the described model version?

Answer: C

Explanation:
In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
https:///model/recommender/Production/invocations.
This allows direct access to the active production version without needing its version number.


NEW QUESTION # 84
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?

Answer: B

Explanation:
A streaming deployment strategy processes incoming data continuously or in microbatches, allowing predictions to be computed as new data arrives. The results can then be written to a table for later querying. This approach balances near-real-time inference with manageable resource use, making it ideal for scenarios where data flows steadily through a pipeline.


NEW QUESTION # 85
Which tool can assist in real-time deployments by packaging software with its own application, tools, and libraries?

Answer: B

Explanation:
Docker is a containerization tool that packages software along with its dependencies, tools, and libraries into a single container. This ensures consistency across environments and is highly useful for real-time deployments, enabling scalable and portable model serving.


NEW QUESTION # 86
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?

Answer: D

Explanation:
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.


NEW QUESTION # 87
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