Databricks-Machine-Learning-Professional Lerntipps, Databricks-Machine-Learning-Professional Prüfungsmaterialien

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

ThemaEinzelheiten
Thema 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
Thema 2
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Thema 3
  • Identify JIT feature values as a need for real-time deployment
  • Describe how to list all webhooks and how to delete a webhook
Thema 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
Thema 5
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
Thema 6
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Thema 7
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 8
  • 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
Thema 9
  • 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
Thema 10
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q44-Q49):

44. Frage
A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

Antwort: A


45. Frage
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

Antwort: D


46. Frage
A Data Scientist is training a binary classification model using LogisticRegression in SparkML on a large dataset stored in a Delta table. After fitting the pipeline, they want to evaluate the model's performance using an appropriate metric and scalable method across the distributed test data using the SparkML API. Which model evaluation strategy will suit their needs?

Antwort: A

Begründung:
BinaryClassificationEvaluator is the Spark ML-native, distributed evaluation API designed specifically for binary classification models. It operates directly on the transformed DataFrame produced by the pipeline and efficiently computes scalable metrics such as areaUnderROC or areaUnderPR without requiring data conversion or custom logic, making it the correct and efficient choice for large, distributed datasets.


47. Frage
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

Antwort: D


48. Frage
Which of the following statements describes streaming with Spark as a model deployment strategy?

Antwort: B


49. Frage
......

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