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

Section	Weight	Objectives
Topic 1: ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring
Topic 2: Model Development	44%	- Distributed training and hyperparameter tuning - Feature Store and automated feature pipelines - Advanced MLflow usage - Scalable ML pipelines with SparkML
Topic 3: Model Deployment	12%	- Custom model serving - Model rollout and version management - Deployment strategies

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Databricks Certified Machine Learning Professional Sample Questions (Q105-Q110):

NEW QUESTION # 105
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?

A. https://<databricks-instance>/model/recommender/Production/invocations
B. https://<databricks-instance>/model-serving/recommender/Production/invocations
C. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
D. https://<databricks-instance>/model/recommender/stage-production/invocations
E. The version number of the model version in Production is necessary to complete this task.

Answer: E

NEW QUESTION # 106
Which of the following describes the concept of MLflow Model flavors?

A. A convention that MLflow Model Registry can use to version models
B. A convention that deployment tools can use to understand the model
C. A convention that MLflow Experiments can use to organize their Runs by project
D. A convention that deployment tools can use to wrap preprocessing logic into a Model
E. A convention that MLflow Model Registry can use to organize its Models by project

Answer: C

NEW QUESTION # 107
A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

A. It registers the new version of the existing model_name model in the MLflow Model Registry.
B. It identifies the name of the logged model in the MLflow Experiment.
C. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
D. It registers a new model called model_name in the MLflow Model Registry.

Answer: D

Explanation:
The parameter registered_model_name=model_name in mlflow.pyfunc.log_model() automatically creates and registers a new model in the MLflow Model Registry if it doesn't already exist. Since this is the first time the model is being run, MLflow will register a new model named model_name and log the current model as version 1 of that registered model.

NEW QUESTION # 108
A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

A. Join the inference table with the fraud_findings table using timestamp_ms as the join key.
B. Populate the client_request_id field with the transaction_id in the model serving request body.
Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.
C. Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
D. Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.

Answer: B

Explanation:
Databricks Model Serving inference tables automatically log the client_request_id field for each request. By populating this field with the existing transaction_id in the request body, the engineer can directly and uniquely join inference predictions with the fraud_findings table using the same identifier, achieving accurate performance monitoring with minimal code changes and no model retraining or redeployment.

NEW QUESTION # 109
What is commonly monitored in deployed ML models?

A. Model drift and prediction quality
B. Code style
C. GPU usage only
D. Notebook formatting

Answer: A

Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.

NEW QUESTION # 110
......

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