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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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	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 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q189-Q194):

NEW QUESTION # 189
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. The same preprocessing logic will automatically be applied when calling predict
B. This approach has no impact when loading the logged Pvfunc model for downstream deployment
C. There is no longer a need for pipeline-like machine learning objects
D. The same preprocessing logic will automatically be applied when calling fit
E. The pvfunc model can be used to deploy models in a parallelizable fashion

Answer: C

NEW QUESTION # 190
A Data Scientist at a company with rapidly increasing sales has deployed a scikit-learn model in production, which is retrained weekly on a single-node cluster. During the most recent retraining, the job failed due to an out-of-memory error. Upon investigation, the Data Scientist discovered that the training data had increased to 700GB as a result of the company's expanding customer base. Which approach will reliably resolve this issue in the long term?

A. Wrap the current model training code as a Spark UDF and apply it to the Spark DataFrame to distribute model training across multiple nodes.
B. Refactor the code to use MLlib across multiple nodes, enabling distributed model training on large volumes of data
C. Increase the size of the current single-node cluster to handle the increase of data while minimizing the need for code refactoring
D. Refactor the code to use the DeepSpeed library to shard the scikit-learn model across multiple nodes, enabling distributed model training on large volumes of data

Answer: B

Explanation:
Spark MLlib is designed for distributed model training on large-scale datasets and can natively handle hundreds of gigabytes of data across multiple nodes. Refactoring to MLlib enables the training workload to scale with data growth, avoids single-node memory limitations, and provides a reliable long-term solution as the company's data continues to expand.

NEW QUESTION # 191
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?

A. context
B. None of these lines of code can be used to successfully complete the code block
C. artifacts
D. model_context

Answer: C

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.

NEW QUESTION # 192
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 returned in a query using the Jobs API
B. The Job ID field in the Jobs page in the UI
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 # 193
Which of the following is an obstacle related to streaming machine learning applications?

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

Answer: D

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 # 194
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