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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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 7	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 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q164-Q169):

NEW QUESTION # 164
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

A. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
Use groupby.applyInPandas.
B. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
C. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.
D. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.

Answer: D

Explanation:
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

NEW QUESTION # 165
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace POST with PUT in the call to http request
B. Replace POST with GET in the call to http request
C. Replace list with view in the endpoint URL
D. There are no necessary changes
E. Replace list with webhooks in the endpoint URL

Answer: B

NEW QUESTION # 166
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 statement 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 fit
B. There is no longer a need for pipeline-like machine learning objects
C. The pvfunc model can be used to deploy models in a parallelizable fashion
D. This approach has no impact when loading the logged Pvfunc model for downstream deployment
E. The same preprocessing logic will automatically be applied when calling predict

Answer: B

NEW QUESTION # 167
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

A. None of these reasons
B. All of these reasons
C. JS is not normalized or smoothed
D. JS is more robust when working with large datasets
E. JS does not require any manual threshold or cutoff determinations

Answer: D

NEW QUESTION # 168
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?

A. mlflow.spark.autolog()
B. mlflow.sklearn.autolog()
C. spark.conf.set("autologging", True)
D. mlflow.autolog()
E. It is not possible to automatically log MLflow runs.

Answer: C

NEW QUESTION # 169
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