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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q182-Q187):

NEW QUESTION # 182
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which type of drift is present in the above scenario?

A. Label drift
B. Prediction drift
C. None of these
D. Feature drift
E. Concept drift

Answer: D

NEW QUESTION # 183
A Data Scientist needs to perform inference on a continuously updated Delta table called sales_data using an MLflow-registered Spark ML pipeline model (catalog.prod.sales_forecaster).
Predictions must be written to a Delta table forecast_results, which must be updated with low latency leveraging a cluster with three executors. They want to maximize the efficient use of their cluster when doing this. Which approach will suit their needs?

Answer: A

Explanation:
This approach uses a Spark Structured Streaming read from the continuously updated Delta table and applies an MLflow-registered Spark UDF for inference. The model execution is distributed across the three executors, enabling parallel, low-latency scoring as new data arrives. Writing the results with writeStream efficiently updates the forecast_results Delta table incrementally, maximizing cluster utilization and aligning with best practices for continuous, scalable batch- stream inference in Databricks.

NEW QUESTION # 184
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. There is no advantage to using batch serving deployments over real-time serving deployments
B. Testing is not possible in real-time serving deployments
C. Querying stored predictions can be faster than computing predictions in real-time
D. Computing predictions in real-time provides more up-to-date results
E. Batch serving has built-in capabilities in Databricks Machine Learning

Answer: E

NEW QUESTION # 185
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A.
B.
C.
D. features_df.write.mode("feature").path("new_table")
E. features_df.write.mode("fs").path("new_table")

Answer: A

NEW QUESTION # 186
Which is a benefit of logging an input example with an MLflow model?

A. The environment requirements are packaged with the MLmodel file
B. An example prediction is available to test post-inference pipelines
C. An example record is available to test serving applications
D. The model evaluation metrics are stored with the model

Answer: C

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.

NEW QUESTION # 187
......

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