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

SectionWeightObjectives
Topic 1: Model Deployment10%- Deployment Strategies
  • 1. Streaming Deployment
  • 2. Real-time Deployment
  • 3. Batch Deployment
- Model Serving
  • 1. Databricks Model Serving
  • 2. Custom serving solutions
Topic 2: Model Lifecycle Management (MLOps)43%- Testing Strategies
  • 1. Data quality testing
  • 2. Model testing and validation
- Automated Workflows
  • 1. CI/CD pipelines for ML
  • 2. Automated retraining workflows
- Monitoring
  • 1. Lakehouse Monitoring for drift detection
  • 2. Model performance monitoring
- Environment Management
  • 1. Databricks Asset Bundles (DABs)
  • 2. Environment configuration and reproducibility
Topic 3: Model Development47%- MLflow Advanced Features
  • 1. Experiment tracking and model management
  • 2. Model registry and versioning
- SparkML and Scaling
  • 1. Ray/Optuna distributed tuning
  • 2. Building scalable ML pipelines with SparkML
  • 3. Distributed training and hyperparameter tuning
- Feature Store
  • 1. Automated feature pipelines
  • 2. Feature Store concepts and usage

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Databricks Certified Machine Learning Professional Sample Questions (Q165-Q170):

NEW QUESTION # 165
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

Answer: A


NEW QUESTION # 166
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)

B)

C) mlflow.log_data(importance_path, "feature-importance.csv")
D) mlflow.log_artifact(importance_path, "feature-importance.csv")
E) None of these code blocks tan accomplish the task.

Answer: D


NEW QUESTION # 167
A Machine Learning Engineer needs to develop a custom anomaly detection model that monitors the internal IT infrastructure of their company. The model takes in compute metrics, logs, and user data and generates a binary prediction. The engineer plans to deploy it as a Databricks Model Serving endpoint. In production there will only be one client calling the endpoint once every
15 seconds. Leadership sees the model as an important part of their operational improvement strategy so maintaining consistent, stable, low latency inference is a requirement while minimizing infrastructure costs. The engineer plans to deploy the endpoint via the MLflow Deployment SDK.
Which endpoint config for the MLflow Deployment SDK should the engineer select?

Answer: D

Explanation:
With a single client making requests every 15 seconds, the traffic is steady and predictable, and low-latency inference is a strict requirement. Disabling scale-to-zero avoids cold start latency, ensuring consistent response times. Selecting a Small workload size minimizes infrastructure costs while still providing sufficient resources for a lightweight binary classification model, making this configuration the best balance between performance, stability, and cost.


NEW QUESTION # 168
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?

Answer: C

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.


NEW QUESTION # 169
A Machine Learning Engineer is using Lakehouse Monitoring to track the performance of ML models deployed in their environment. They want to monitor significant distributional drift in categorical features with a metric bounded on [0,1] for easy interpretation. Which statistical method should they use?

Answer: D


NEW QUESTION # 170
......

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