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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	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 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q84-Q89):

NEW QUESTION # 84
A Data Scientist has created a sales forecasting model, named sales-forecasting. The model is deployed to a model serving endpoint with a schema. They need to invoke this model using Python and would prefer to use a SDK function to make the request. Which method suits these requirements?

A. Use the built in ai_query function and provide the endpoints and request parameters.
B. Import the MLflow Deployments class and use the predict method and provide the endpoint and input parameters.
C. Make an API request to the MODEL_VERSION_URI and provide the dataframe_split as the request parameter.
D. Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.

Answer: B

Explanation:
The MLflow Deployments SDK provides a predict method specifically designed to invoke Databricks model serving endpoints from Python. This method abstracts the HTTP request details, allows specifying the endpoint name directly, and cleanly passes input parameters that conform to the model's serving schema, making it the preferred SDK-based approach.

NEW QUESTION # 85
Which of the following is an obstacle related to streaming machine learning applications?

A. Out-of-order data
B. All of these
C. None of these
D. End-to-end fault tolerance

Answer: B

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 86
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

A. MLflow Tracking
B. MLflow Models
C. Model Registry
D. MLflow Projects

Answer: A

Explanation:
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)

NEW QUESTION # 87
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 formatfdelta") with format("stream")
B. Replace predict with a stream-friendly prediction function
C. Replace "inference" with the path to the location of the Delta table
D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
E. Replace spark.read with spark.readStream

Answer: D

NEW QUESTION # 88
A machine learning engineer wants to load the data from the very first version of a Delta table from location path. Which of the following lines of code can be used to accomplish this task?

A. spark.read.format("delta").option("version", 1).load(path)
B. spark.read.format("delta").option("versionAsOf", 0).load(path)
C. spark.read.format("delta").option("version", 0).load(path)
D. spark.read.format("delta").option("versionAsOf", 1).load(path)

Answer: D

Explanation:
In Delta Lake, the option("versionAsOf", <version_number>) parameter is used to load a specific version of a Delta table.
Since Delta table versions are 1-indexed (the first version is version 1), using .option("versionAsOf", 1) correctly loads the very first version of the table.

NEW QUESTION # 89
......

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