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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	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 10	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q157-Q162):

NEW QUESTION # 157
A machine learning engineer has developed a random forest model using scikit-learn and registered the model using MLflow. They now want to deploy that model in parallel. Which of the following operations can they use to create a function they can use to deploy the registered scikit- learn model in parallel?

A. mlflow.sklearn.spark.udf
B. It is not possible to deploy a scikit-learn model in parallel.
C. mlflow.pyfunk.spark.udf
D. mlflow.spark.pandas_udf

Answer: A

Explanation:
The correct operation is mlflow.sklearn.spark_udf, which creates a Spark UDF from a registered scikit-learn model. This allows the model to be applied in parallel across a Spark DataFrame, enabling scalable batch inference using Spark.

NEW QUESTION # 158
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 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 159
Which of the following is a benefit of logging a model signature with an MLflow model?

A. The model will have a unique identifier in the MLflow experiment
B. The schema of input data will be converted to match the signature
C. The model will be secured by the user that developed it
D. The model can be deployed using real-time serving tools
E. The schema of input data can be validated when serving models

Answer: B

NEW QUESTION # 160
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: E

NEW QUESTION # 161
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

A. JS is more robust when working with large datasets
B. None of these reasons
C. All of these reasons
D. JS does not require any manual threshold or cutoff determinations
E. JS is not normalized or smoothed

Answer: A

NEW QUESTION # 162
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