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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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	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 4	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 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	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 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 (Q31-Q36):

NEW QUESTION # 31
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Development. Staging. Production
B. None. Staging. Production. Archived
C. Staging. Production. Archived
D. None. Staging. Production
E. Development. Staging. Production. Archived

Answer: A

NEW QUESTION # 32
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace "inference" with the path to the location of the Delta table
B. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
C. Replace predict with a stream-friendly prediction function
D. Replace spark.read with spark.readStream
E. Replace formatfdelta") with format("stream")

Answer: B

NEW QUESTION # 33
What tool helps avoid feature mismatch between training and inference?

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

Answer: B

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

NEW QUESTION # 34
Which of the following describes batch deployment for machine learning projects?

A. None of these describe batch deployment for machine learning projects
B. Predictions are computed and delivered as soon as feature values are available
C. Predictions are computed immediately as data arrives and stored for later querying
D. Predictions are computed prior to delivery and stored for later querying

Answer: D

Explanation:
In batch deployment, predictions are precomputed at scheduled intervals and stored for later querying. This approach is ideal when real-time inference is not required and when predictions can be generated in bulk ahead of time.

NEW QUESTION # 35
A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?

A. Create a derived metric for the composite_risk_rating calculation and create a drift metric on this derived metric.
B. Create an aggregate metric for composite_risk_rating and configure a separate drift metric to monitor changes.
C. Use a scheduled notebook to calculate the composite score and manually insert both the score and drift values into the profile metrics table.
D. Create two separate aggregate metrics: one for the composite score calculation and another for drift detection.

Answer: A

Explanation:
Lakehouse Monitoring supports derived metrics that are computed from existing profile metrics using custom expressions. By defining a derived metric for the composite_risk_rating using the specified weights, the composite score becomes a first-class metric in the monitoring framework.
A drift metric can then be directly configured on this derived metric to compare current values against the baseline, fulfilling both the composite calculation and drift monitoring requirements in a native, governed way.

NEW QUESTION # 36
......

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