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

Section	Objectives
Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Dimensionality reduction 2. Clustering techniques - Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics
MLflow and Experiment Tracking	- Experiment management 1. Model comparison and selection 2. Tracking runs and parameters - Model registry 1. Versioning and lifecycle management
Machine Learning Workflow on Databricks	- Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks - End-to-end ML pipelines 1. Pipeline construction and orchestration 2. Reusable ML workflows
MLOps, Monitoring, and Governance	- Governance and compliance 1. Feature Store usage and management 2. Model lifecycle governance - Model monitoring 1. Data drift detection 2. Performance tracking in production
Model Deployment and Serving	- Model deployment strategies 1. Batch inference vs real-time inference 2. Databricks Model Serving

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Databricks Databricks-Machine-Learning-Professional Databricks Certified Machine Learning Professional Questions - With 25% Discount Offer [2026]

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Databricks Certified Machine Learning Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
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. One-way Chi-squared Test
B. Jenson-Shannon distance
C. Two-way Chi-squared Test
D. Kolmogorov-Smirnov (KS) test
E. None of these

Answer: A

NEW QUESTION # 64
Which MLflow operation can be used to log a plot that was generated during an MLflow run?

A. mlflow.log_metric
B. None of these operations can accomplish the task.
C. mlflow.log_figure
D. mlflow.log_visualization

Answer: C

Explanation:
The mlflow.log_figure() operation is used to log plots or figures generated during an MLflow run. It takes a Matplotlib, Plotly, or similar figure object and saves it as an artifact in the MLflow tracking server, enabling visualization and later retrieval from the MLflow UI.

NEW QUESTION # 65
A Machine Learning Engineer is tasked with implementing a simple solution, requiring as little code as possible, to monitor a regression model and ensure that the R2 score does not exceed a certain threshold. If the threshold is exceeded, the solution needs to send an email to a distribution list. Which proposed solution meets the criteria?

A. Configure Lakehouse Monitoring using the Inference profile type. Create a Workflow that runs on a schedule and executes a SQL query that evaluates the R2 score in the profile table. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.
B. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Alert that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
C. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Query that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
D. Create a Workflow that runs on a schedule. The Workflow executes a SQL Query that calculates the R2 score. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.

Answer: B

Explanation:
Lakehouse Monitoring with the Inference profile already computes regression performance metrics such as R2 and stores them in Delta tables. A Databricks SQL Alert can directly evaluate the R2 value against a threshold on a schedule and send an email notification when the condition is met. This requires minimal code and avoids additional workflows or custom logic, making it the simplest and most maintainable solution.

NEW QUESTION # 66
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. Enable mlflow.autolog() at the beginning of the code and rely on it to automatically detect and log custom metrics, parameters, and visualization files.
C. 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.
D. Write all custom metrics and parameters to stdout during training and configure MLflow's tracking server to scrape these outputs.

Answer: C

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 # 67
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B.
C.
D.
E. It is not possible to deploy a scikit-learn model on a Spark DataFrame.

Answer: A

NEW QUESTION # 68
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