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Databricks Databricks-Machine-Learning-Professional Exam Sample Questions


Question # 1

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. VERSION
B. DESCRIBE
C. HISTORY
D. DESCRIBE HISTORY


D. DESCRIBE HISTORY
Explanation:

The DESCRIBE HISTORY command can be used to view the commit history of a Delta table, including the schema changes, operations, and timestamps. This command can help identify when a column was dropped from the feature set and by which operation. The other commands are either invalid or do not provide the required information. References:

Delta Lake - View Commit History
Databricks Certified Machine Learning Professional Exam Guide - Section 1: Experimentation - Data Management




Question # 2

A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in themodel_urivariable and its Run ID in therun_idvariable. They have also determined that the model was logged with the name"model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name"best_model". Which of the following lines of code can they use to register the model to the MLflow Model Registry?
A. mlflow.register_model(model_uri, "best_model")
B. mlflow.register_model(run_id, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(model_uri, "model")
E. mlflow.register_model(f"runs:/{run_id}/model")


A. mlflow.register_model(model_uri, "best_model")
Explanation:

The mlflow.register_model function takes two arguments: model_uri and name. The model_uri is the URI of the model that was logged to MLflow, which can be obtained from the best run object. The name is the name of the registered model in the MLflow Model Registry. Therefore, the correct line of code to register the model is:

mlflow.register_model(model_uri, “best_model”)

This will create a new registered model with the name “best_model” and register the model version from the best run as the first version of that model.

References:

[mlflow.register_model — MLflow 1.22.0 documentation]
[MLflow Model Registry — Databricks Documentation]
[Manage MLflow Models — Databricks Documentation] Message has links.




Question # 3

Which of the following is a simple statistic to monitor for categorical feature drift?
A. Mode
B. None of these
C. Mode, number of unique values, and percentage of missing values
D. Percentage of missing values
E. Number of unique values


C. Mode, number of unique values, and percentage of missing values
Explanation:

Categorical feature drift is a change in the distribution of the input data over time for categorical features, which can affect the performance and accuracy of the model. Monitoring categorical feature drift is important to ensure that the model is still valid and reliable for the current data. One simple statistic to monitor for categorical feature drift is the combination of mode, number of unique values, and percentage of missing values for each categorical feature. These statistics can provide a quick overview of the changes in the data distribution, such as the most frequent category, the diversity of categories, and the quality of data. If these statistics deviate significantly from the baseline values, it may indicate a categorical feature drift. However, these statistics may not capture all the nuances of the data distribution, such as the relative frequencies of different categories, the similarity of categories, etc. Therefore, other methods, such as statistical tests or information-theoretic measures, may be needed to complement the simple statistics and provide a more comprehensive analysis of the categorical feature drift123 References:

Monitoring Feature Drift - Databricks
Drift Metrics: How to Select the Right Metric to Analyze Drift
Detect data drift on datasets (preview) - Azure Machine Learning




Question # 4

Which of the following describes concept drift?
A. Concept drift is when there is a change in the distribution of an input variable
B. Concept drift is when there is a change in the distribution of a target variable
C. Concept drift is when there is a change in the relationship between input variables and target variables
D. Concept drift is when there is a change in the distribution of the predicted target given by the model
E. None of these describe Concept drift


C. Concept drift is when there is a change in the relationship between input variables and target variables
Explanation:

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc

References:

Concept drift - Wikipedia
A Gentle Introduction to Concept Drift in Machine Learning
Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.
Data Drift vs. Concept Drift: What Is the Difference?




Question # 5

A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which of the following locations in Databricks will show these data visualizations?
A. The MLflow Model RegistryModel paqe
B. The Artifacts section of the MLflow Experiment page
C. Logged data visualizations cannot be viewed in Databricks
D. The Artifacts section of the MLflow Run page
E. The Figures section of the MLflow Run page


D. The Artifacts section of the MLflow Run page
Explanation:

To view the data visualizations that are logged with each run, you can go to the Artifacts section of the MLflow Run page in Databricks. The Artifacts section shows the files and directories that are logged as artifacts for a run. You can browse the artifact hierarchy and preview the files, such as images, text, or HTML1. You can also download the artifacts or copy their URIs for further use2. The other options are incorrect because:

Option A: The MLflow Model Registry Model page shows the information and metadata of a registered model, such as its name, description, versions, stages, and lineage. It does not show the data visualizations that are logged with each run3.

Option B: The Artifacts section of the MLflow Experiment page shows the artifacts that are logged for an experiment, not for a specific run. It does not allow you to preview the files or browse the artifact hierarchy4.

Option C: Logged data visualizations can be viewed in Databricks using the Artifacts section of the MLflow Run page1.

Option E: There is no Figures section of the MLflow Run page in Databricks. The Figures section is only available in the open source MLflow UI, which shows the plots that are logged as figures for a run5. References: View run artifacts, Log, list, and download artifacts, Manage models, View experiment artifacts, Logging Visualizations with MLflow




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