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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 7	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 8	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 9	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q147-Q152):

NEW QUESTION # 147
A Machine Learning Engineer needs to build a credit risk model using Databricks. Due to strict data governance, production data cannot be accessed from development or staging environments. To manage MLOps, the engineer uses a "deploy code" strategy with separate development, staging, and production environments mapped to different catalogs in Unity Catalog. The CI/CD pipeline automates environment transitions. What is the primary architectural component promoted from staging to production to generate the final, production-ready model in this scenario?

A. A containerized image of the complete inference pipeline, including feature transformations.
B. The model artifact trained and validated in the staging environment on a subset of the data.
C. The CI/CD-approved model training source code, which will be executed in the production environment on the full production dataset.
D. The model version entry in the Unity Catalog that has been transitioned from the "Staging" to the
"Production" alias.

Answer: C

Explanation:
In a deploy-code strategy, models are not promoted as trained artifacts across environments.
Instead, the approved training code is promoted through CI/CD and re-executed in the production environment against production data to produce the final model. This approach satisfies strict data governance requirements by ensuring production data is only accessed in production while maintaining reproducibility and traceability.

NEW QUESTION # 148
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
B. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
C. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
D. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.

Answer: A

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 149
A Data Scientist is training a model to predict whether a customer will purchase a new product.
They are experimenting with many algorithm types (e.g., Linear Regression, Logistic Regression, Decision Trees) and hundreds of hyperparameter combinations. They are using MLflow to track their runs, but managing and distinguishing between runs has become difficult. They need an MLFIow technique that will organize and trace these experiments and that will follow commonly- accepted design patterns. Which technique will fulfill these requirements?

A. Log runs for each algorithm type to different MLflow Tracking servers to ensure the runs do not get mixed up.
B. Set each algorithm type as a parent run and use nested runs for different hyperparameter settings within that parent run.
C. Use MLflows Automatic Logging feature to reduce the number of explicit log statements within each run to increase code readability.
D. Use AutoML to automatically test a variety of different algorithms and models.

Answer: B

Explanation:
Using parent runs for each algorithm and nested runs for the associated hyperparameter experiments follows a widely accepted MLflow experiment design pattern. This structure makes it easy to organize, compare, and trace large numbers of related runs, improving experiment management and interpretability when exploring many models and hyperparameter configurations.

NEW QUESTION # 150
A data scientist would like to switch from manually using MLflow logging to MLflow Autologging for all machine learning libraries used in a notebook.
They begin by adding mlflow.autolog()to the top of the below code block:

The data scientist is now trying to determine which line of code will kick off the MLflow Autologging process.
Which line of code within the above code block will start the MLflow Autologging process?

A. rf_model = rf.fit(X_train, y_train)
B. with mlflow.start_run(run_name="ML Project") as run:
C. mlflow.log_metric("training_mse", mse)
D. mlflow.sklearn.log_model(rf_model, "rf_model")

Answer: A

Explanation:
The MLflow Autologging process is triggered when a model's training function is called. In this case, rf.fit(X_train, y_train) starts the autologging process because mlflow.autolog() automatically tracks parameters, metrics, and the model when a supported estimator (like RandomForestRegressor) is fitted. The context manager mlflow.start_run() only initiates an MLflow run, but the actual autologging begins during the .fit() execution.

NEW QUESTION # 151
Which Spark ML class supports automated hyperparameter tuning?

A. PipelineModel
B. CrossValidator
C. ParamGridBuilder
D. BinaryClassificationEvaluator

Answer: B

Explanation:
CrossValidator performs:
parameter search
k-fold cross validation.

NEW QUESTION # 152
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