Databricks-Machine-Learning-Professional Exam Overview & Exam Databricks-Machine-Learning-Professional Exercise

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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 5	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 6	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q112-Q117):

NEW QUESTION # 112
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. This can only be viewed in the MLflow Experiments UI
B. client.pyfunc.load_model(model_uri)
C. mlflow.load_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)
E. client.list_artifacts(run_id)["feature-importances.csv"]

Answer: D

NEW QUESTION # 113
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?

A. None of these
B. Feature drift
C. Concept drift
D. Label drift
E. Prediction drift

Answer: B

NEW QUESTION # 114
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. Edge/on-device
B. Batch
C. Streaming
D. None of these strategies will accomplish the task.
E. Real-time

Answer: E

NEW QUESTION # 115
A Machine Learning Engineer wants to monitor the quality and stability of their machine learning model's predictions over time. They have a Delta table, retail_inference_log, which records each model prediction along with input features, a timestamp, and (when available) the true label. They need to detect data drift and monitor model performance trends using Databricks Lakehouse Monitoring, ensuring that alerts are triggered if the distribution of predictions or input features changes significantly. Which approach will set up monitoring for this use case?

A. Create a monitor with the Time Series profile on the retail_inference_log table, specifying the timestamp column and including model input, prediction columns and the true label column. This will track drift in features and predictions over time, and model performance could also be tracked using a custom metric.
B. Create a monitor with the Inference profile on the retail_inference_log table, specifying the timestamp column and the columns for model inputs, predictions, and labels. Configure the monitor to compute drift and performance metrics over time windows.
C. Create a monitor with the Inference profile on the retail_inference_log table, and specify a recent batch of production data as the baseline table for drift detection. Use this recent production data to compare against new data for drift and performance monitoring.
D. Create a monitor with the Snapshot profile on the retail_inference_log table, so that metrics are calculated over the entire table each time the monitor runs and therefore is able to compare new values with previous ones to compute data drift.

Answer: B

Explanation:
The Inference profile is specifically designed for monitoring production inference logs. By configuring it on the inference table with the timestamp, input feature columns, prediction column, and label column, Databricks Lakehouse Monitoring can automatically compute prediction drift, input feature drift, and model performance metrics over rolling time windows, and trigger alerts when significant distribution changes or performance degradation are detected.

NEW QUESTION # 116
Which MLflow function registers a model to the registry?

A. mlflow.deploy_model()
B. mlflow.log_param()
C. mlflow.register_model()
D. mlflow.create_model()

Answer: C

Explanation:
mlflow.register_model() promotes a model into the Model Registry.

NEW QUESTION # 117
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