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

Topic
Details

Topic 1

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 2

Identify which code block will trigger a shown webhook
Describe the basic purpose and user interactions with Model Registry

Topic 3

Describe model serving deploys and endpoint for every stage
Identify scenarios in which feature drift and
or label drift are likely to occur

Topic 4

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 5

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 6

Describe the advantages of using the pyfunc MLflow flavor
Manually log parameters, models, and evaluation metrics using MLflow

Topic 7

Identify less performant data storage as a solution for other use cases
Describe why complex business logic must be handled in streaming deployments

Topic 8

Identify the requirements for tracking nested runs
Describe an MLflow flavor and the benefits of using MLflow flavors

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

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 11

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 (Q129-Q134):

NEW QUESTION # 129
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
B. HISTORY
C. TIMESTAMP
D. VERSION
E. DESCRIBE HISTORY

Answer: E

NEW QUESTION # 130
A Data Scientist is training a complex gradient-boosted model for fraud detection. The model uses dynamic threshold tuning during training and generates custom visualizations of feature drift. To ensure reproducibility and collaboration, they need to programmatically track:
- Custom metrics (e g., adjusted_f1 for threshold variations)
- Hyperparameters from nested configuration files
- Drift visualization plots as PDFs
Which approach implements this tracking in MLflow?

A. Store hyperparameters in a YAML file, save metrics to a CSV, and use mlflow.log_artifact() to upload both to the tracking server as artifacts.
B. Use mlflow.log_metric("adjusted_f1", value),mlflow.log_params(nested_config), and mlflow.log_artifact("drift_plot.pdf") within an active MLflow run context, while enabling mlflow.autolog() to capture model-specific metadata automatically.
C. Write all custom metrics and parameters to stdout during training and configure MLflow's tracking server to scrape these outputs.
D. Enable mlflow.autolog() at the beginning of the code and rely on it to automatically detect and log custom metrics, parameters, and visualization files.

Answer: B

Explanation:
Explicitly using mlflow.log_metric, mlflow.log_params, and mlflow.log_artifact within an active MLflow run provides precise, programmatic control over tracking custom metrics, complex hyperparameter configurations, and generated artifacts such as PDF visualizations. Enabling mlflow.autolog alongside this captures standard model metadata automatically while still allowing full flexibility for advanced, custom tracking needs.

NEW QUESTION # 131
Label drift occurs where there is a change in which element?

A. The distribution of a target variable
B. The relationship between input variables and target variables
C. The distribution of the predicted target given by the model
D. The distribution of an input variable

Answer: A

Explanation:
Label drift refers to a change in the distribution of the target variable over time. This means the frequencies or proportions of classes or target values shift, which can impact model performance even if the input feature distributions remain unchanged.

NEW QUESTION # 132
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors can only be used for logging models
B. Built-in library-specific flavors allow models to be exported as library objects
C. Built-in library-specific flavors allow models to be used with any library/span>
D. Built-in library-specific flavors are required for model signature use

Answer: B

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 133
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. Kolmogorov-Smirnov (KS) test
B. Jenson-Shannon distance
C. Two-way Chi-squared Test
D. One-way Chi-squared Test
E. None of these

Answer: D

NEW QUESTION # 134
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