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

Topic	Details
Topic 1	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 2	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 3	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 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	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 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 8	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 9	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q179-Q184):

NEW QUESTION # 179
A Machine Learning Engineer has created a custom PyFunc model wrapper for a fraud detection system that needs to be registered in Unity Catalog under the schema risk_models in the production catalog. The model requires specific dependencies and must be accessible for governance and version control. The engineer is working on a dedicated cluster with Unity Catalog enabled, but the model registration is failing. Why is the model failing to be registered in this case?

A. The model artifacts must be stored in DBFS rather than Unity Catalog volumes.
B. The PyFunc model must inherit from mlflow.sklearn.Model instead of mlflow.pyfunc.PythonModel.
C. The cluster must be configured with shared access mode instead of dedicated mode.
D. The model signature must be provided, as Unity Catalog requires all models to have signatures.

Answer: C

Explanation:
Unity Catalog model registration requires the cluster to run in shared access mode. Dedicated (single-user) clusters do not support registering models into Unity Catalog because governance, lineage, and access control enforcement rely on shared-mode execution. As a result, even if Unity Catalog is enabled, model registration will fail when attempted from a dedicated cluster.

NEW QUESTION # 180
A Data Scientist has been performing hyperparameter tuning using Ray Tune with grid search.
After team discussions, they decide to switch to Bayesian optimization to more efficiently explore the parameter space.
Their current code is:

How can they implement this change?

Answer: B

Explanation:
Bayesian optimization in Ray Tune requires defining a continuous or discrete search space (such as tune.randint) and explicitly configuring a Bayesian search algorithm. Using tune.randint defines a probabilistic parameter domain, and setting search_alg to BayesOptSearch enables Bayesian optimization to efficiently explore the space based on past trial results, rather than exhaustively enumerating all values as in grid search.

NEW QUESTION # 181
A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

A. Set MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_TD environment variables before initializing Ray.
B. Install the MLflow Ray plugin using %pip install mlflow-ray and configure the workspace connection.
C. Configure DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before calling setup_ray_cluster().
D. Enable MLflow autologging with mlflow.ray.autolog() and set the tracking server URI.

Answer: C

Explanation:
When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.

NEW QUESTION # 182
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

A. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
B. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.
C. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
Use groupby.applyInPandas.
D. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.

Answer: B

Explanation:
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

NEW QUESTION # 183
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?

A. [Structured Streaming
B. AutoML
C. Spark UDFs
D. MLflow
D Delta Lake

Answer: C

NEW QUESTION # 184
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