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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
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 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 7
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 8
  • 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 (Q159-Q164):

NEW QUESTION # 159
Which of the following lists all of the model stages are available in the MLflow Model Registry?

Answer: C


NEW QUESTION # 160
A machine learning engineer has created a webhook with the following code block:

Which code block will trigger this webhook to run the associate job?

Answer: D

Explanation:
The webhook in the first image is configured to trigger on the
MODEL_VERSION_TRANSITIONED_TO_PRODUCTION event. The chosen code uses
client.transition_model_version_stage(...) to move the model version to "Production", which matches the event type in the webhook and will correctly trigger the associated job.


NEW QUESTION # 161
A Machine Learning Engineer is working with a Spark DataFrame containing 100 million rows of retail transactions across thousands of stores. Each store requires its own demand forecasting model using the same scikit-learn pipeline. They want to train and apply these models in parallel for each store without collecting the data to the driver. Which approach will do this?

Answer: D

Explanation:
groupBy().applyInPandas() enables grouped Pandas UDFs that receive each store's data as a Pandas DataFrame on the executors. This allows training and applying a separate scikit-learn model per store in parallel without collecting data to the driver, making it the correct and scalable approach for large Spark DataFrames.


NEW QUESTION # 162
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which of the following code blocks can they use to accomplish the task?

Answer: A


NEW QUESTION # 163
A Machine Learning Engineer is using joblibspark and MLflowCallback to perform a distributed hyperparameter tuning experiment via Optuna. Assuming they have a single objective they are optimizing for, what will be the default sampler implemented by Optuna?

Answer: A

Explanation:
For single-objective optimization, Optuna uses the Tree-structured Parzen Estimator (TPE) sampler by default. TPESampler is a Bayesian optimization method that efficiently explores the hyperparameter space by modeling promising parameter regions, making it well suited for scalable hyperparameter tuning with joblibspark and MLflowCallback.


NEW QUESTION # 164
......

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