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

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 2	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 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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
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
Topic 10	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q20-Q25):

NEW QUESTION # 20
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

Which lines of code can be used to fill in the blank so that the code block accomplishes the task?

A. "MODEL_VERSION_CREATED"
B. "MODEL_VERSION_TRANSITIONED_TO_STAGING",
"MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
C. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
D. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
E. "MODEL_VERSION_TRANSITIONED_STAGE"

Answer: D

NEW QUESTION # 21
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.
B. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.
C. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
D. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.

Answer: A

Explanation:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

NEW QUESTION # 22
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Feature Store
B. Models
C. Model Serving
D. Experiments
E. Model Registry

Answer: C

NEW QUESTION # 23
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. There is no way to accomplish this task with fs
B. fs.write_table
C. fs.read_table
D. fs.create_table
E. fs.get_table

Answer: D

NEW QUESTION # 24
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. Use MLflows Automatic Logging feature to reduce the number of explicit log statements within each run to increase code readability.
B. Use AutoML to automatically test a variety of different algorithms and models.
C. Set each algorithm type as a parent run and use nested runs for different hyperparameter settings within that parent run.
D. Log runs for each algorithm type to different MLflow Tracking servers to ensure the runs do not get mixed up.

Answer: C

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 # 25
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