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

TopicDetails
Topic 1
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
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 model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 4
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 5
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 6
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
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
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Topic 9
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 10
  • 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 11
  • 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

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Databricks Certified Machine Learning Professional Sample Questions (Q144-Q149):

NEW QUESTION # 144
A machine learning engineer is migrating a machine learning pipeline to use Data bricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs.
They are using the following code block as part of their solution:

Assuming that this is the first time that the model is being run, which statement describes the impact of the registered_model_name=model_name parameter?

Answer: B

Explanation:
The parameter registered_model_name=model_name in mlflow.pyfunc.log_model() automatically creates and registers a new model in the MLflow Model Registry if it doesn't already exist. Since this is the first time the model is being run, MLflow will register a new model named model_name and log the current model as version 1 of that registered model.


NEW QUESTION # 145
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

Answer: C

Explanation:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.


NEW QUESTION # 146
What is commonly monitored in deployed ML models?

Answer: A

Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.


NEW QUESTION # 147
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URls can be used to query the described model version?

Answer: B

Explanation:
In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
https:///model/recommender/Production/invocations.
This allows direct access to the active production version without needing its version number.


NEW QUESTION # 148
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

Answer: B


NEW QUESTION # 149
......

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