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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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	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 4	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 5	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 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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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Databricks Certified Machine Learning Professional Sample Questions (Q54-Q59):

NEW QUESTION # 54
In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

A. The Job ID field in the Jobs page in the UI
B. The Job ID field returned in a query using the Jobs API
C. The Job ID field in the Job details section of the specific Job page in the UI
D. All of these approaches

Answer: D

Explanation:
A Databricks Job ID can be obtained in multiple ways - it is displayed directly in the Jobs page, in the Job details section of a specific Job, and can also be retrieved programmatically through the Databricks Jobs API. Any of these methods can be used to supply the Job ID when configuring an MLflow Model Registry Webhook.

NEW QUESTION # 55
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A. mlflow.log_data(importance_path, "feature-importance.csv")
B. None of these code blocks tan accomplish the task.
C.
D. mlflow.log_artifact(importance_path, "feature-importance.csv")
E.

Answer: E

NEW QUESTION # 56
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Write as a Parquet file
B. Tuning the file size
C. Bin-packing
D. Data skipping
E. Z-Ordering

Answer: B

NEW QUESTION # 57
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

A. Batch
B. None of these strategies will accomplish the task.
C. Streaming
D. Real-time
E. Edge/on-device

Answer: C

NEW QUESTION # 58
Which of the following describes the concept of MLflow Model flavors?

A. A convention that MLflow Experiments can use to organize their Runs by project
B. A convention that deployment tools can use to understand the model
C. A convention that deployment tools can use to wrap preprocessing logic into a Model
D. A convention that MLflow Model Registry can use to organize its Models by project
E. A convention that MLflow Model Registry can use to version models

Answer: A

NEW QUESTION # 59
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