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

Topic	Details
Topic 1	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 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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
Topic 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 10	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project". Which MLflow operation can the machine learning engineer use to accomplish this task?

A. MlflowClient.update_registered_model
B. MlflowClient.get_model_version
C. mlflow.register_model
D. The machine learning engineer needs to create an entirely new MLflow Model Registry model
E. mlflow.add_model_version

Answer: A

NEW QUESTION # 30
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which SQL command can be used to accomplish this task?

A. DESCRIBE HISTORY
B. VERSION
C. DESCRIBE
D. HISTORY
E. TIMESTAMP

Answer: A

NEW QUESTION # 31
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

A. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
B. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
D. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute.

Answer: A

Explanation:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.

NEW QUESTION # 32
A machine learning engineer is monitoring label values for a production machine learning classification model. The engineer believes that the relative prevalence of the classes is becoming changing in more recent data. Which tool can the machine learning engineer use to assess their theory?

A. Jenson-Shannon distance
B. Kolmogorov-Smirnov (KS) test
C. One-way Chi-squared Test
D. Two-way Chi-squared Test

Answer: C

Explanation:
A One-way Chi-squared Test is used to compare observed class distributions with expected distributions to determine if there has been a significant shift. This makes it suitable for detecting label drift in classification tasks, where the relative prevalence of classes may be changing over time.

NEW QUESTION # 33
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. DESCRIBE HISTORY
B. VERSION
C. DESCRIBE
D. HISTORY
E. TIMESTAMP

Answer: A

NEW QUESTION # 34
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