New Databricks-Machine-Learning-Professional Test Question - Test Databricks-Machine-Learning-Professional Simulator

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

TopicDetails
Topic 1
  • 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 2
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 3
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Topic 4
  • 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 5
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
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 less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
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 (Q94-Q99):

NEW QUESTION # 94
A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns:

However, instances of this class are unable to compute predictions.
Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?

Answer: A

Explanation:
The issue is that the predict() method does not apply the same preprocessing as the fit() method.
During training, the model uses preprocessed data (via self.preprocess_input()), but during prediction, it directly uses raw input. This mismatch causes prediction errors because the model expects preprocessed input.
By updating the predict() method to call self.preprocess_input(model_input.copy()), both training and prediction use consistent feature transformations, allowing predictions to be computed successfully.


NEW QUESTION # 95
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

Answer: D


NEW QUESTION # 96
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?

Answer: D


NEW QUESTION # 97
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a run with run ID run_id for the experiment with experiment ID exp_id, the data scientist wants to programmatically return the logged metrics for run_id. They have an active MLflow Client client and an active Spark session spark. Which lines of code can be used to return the logged metrics for run_id?

Answer: C

Explanation:
The correct way to retrieve logged metrics for a specific run using the MLflow Client is client.get_run(run_id).data.metrics. This returns a dictionary of all metrics logged for that run. The method in the image (mlflow.search_runs(...)) is for querying across multiple runs, not for accessing a specific run's metrics.


NEW QUESTION # 98
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)

B)

C) mlflow.log_data(importance_path, "feature-importance.csv")
D) mlflow.log_artifact(importance_path, "feature-importance.csv")
E) None of these code blocks tan accomplish the task.

Answer: D


NEW QUESTION # 99
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