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

Topic	Details
Topic 1	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 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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	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 8	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

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Databricks Certified Machine Learning Professional Sample Questions (Q96-Q101):

NEW QUESTION # 96
A Data Scientist at an online gaming company is creating a model to predict player churn. The company currently collects terabytes of player activity logs daily, which are stored in Databricks and processed for daily reporting. The Data Scientist has completed feature engineering and the resulting data is saved as a Delta Table with a size of 500GB. They need to next build the model for the most performant and cost-effective performance for Databricks. Which approach will do this?

A. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a single-node Databricks cluster.
B. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.
C. Load the feature data as a Spark DataFrame and train the model using SparkML's RandomForestClassifier on a multi-node Databricks cluster.
D. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a multi-node Databricks cluster.

Answer: C

Explanation:
A 500GB Delta Table is far beyond what is practical to load into a single pandas DataFrame, and scaling pandas-based scikit-learn training across nodes is not the right fit for this workload. Using a Spark DataFrame with Spark ML's RandomForestClassifier leverages distributed data processing and distributed model training on a multi-node cluster, which is the most performant and cost-effective approach for large tabular datasets in Databricks.

NEW QUESTION # 97
What tool helps avoid feature mismatch between training and inference?

A. Model Registry
B. Feature Store
C. Spark SQL
D. MLflow Projects

Answer: B

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 98
Which approach is best for scoring large historical datasets?

A. Local Python script
B. Real-time REST API
C. Batch inference with Spark
D. Manual predictions

Answer: C

Explanation:
Spark allows distributed batch scoring across massive datasets.

NEW QUESTION # 99
A Machine Learning Engineer wants to use on-demand features to train a model. They have a Python UDF, which relies on data fetched from a FeatureLookup. However, in online serving, the pipeline falls when the lookup ID is not found. Which solution will resolve this issue?

A. Log the model correctly via the FeatureEngineeringClient, so it automatically evaluates on- demand features.
B. Register the UDF to Unity Catalog, so None values are handled automatically.
C. Add a default ID when defining the FeatureLookup, so it does not return None values.
D. Update the UDF so that it handles both NaNs and None values.

Answer: D

Explanation:
When an on-demand feature relies on a FeatureLookup, missing lookup keys can result in None values during online serving. Updating the Python UDF to explicitly handle None and NaN values ensures the feature computation is robust to missing lookups, preventing runtime failures while preserving correct behavior during both training and online inference.

NEW QUESTION # 100
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. Install the MLflow Ray plugin using %pip install mlflow-ray and configure the workspace connection.
B. Enable MLflow autologging with mlflow.ray.autolog() and set the tracking server URI.
C. Configure DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before calling setup_ray_cluster().
D. Set MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_TD environment variables before initializing Ray.

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 # 101
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