AutoML and ML Runtime MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   During an AutoML run in Databricks, how does the system determine which model to present as the best result?

   • AIt uses the first algorithm that completes training
   • BIt selects the model with the fastest training time
   • CIt always selects the most complex algorithm available
   • DIt compares multiple algorithms and hyperparameter configurations and selects the one with the best evaluation metric
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2. 12foundation · easy

   A machine learning team needs to ensure that their training code runs identically across development and production clusters. Which benefit of Databricks ML Runtime is most directly relevant to this requirement?

   • ABuilt-in data visualization tools
   • BAutomatic hyperparameter tuning
   • CReproducibility through consistent library versions
   • DAutomatic model deployment to production
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4. 13foundation · easy

   Which of the following is a key advantage of using AutoML in the model development process?

   • AIt reduces manual effort by automating repetitive tasks like feature engineering and model tuning
   • BIt automatically deploys models without any configuration
   • CIt eliminates the need for data cleaning
   • DIt guarantees the model will be 100% accurate
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5. 14expert · hard

   A company is planning to deploy a model to production. They want to ensure that the model can be retrained periodically with minimal manual intervention. Which combination of Databricks features best supports this?

   • AUse a scheduled notebook that manually runs a fixed training script.
   • BUse AutoML for automated retraining and ML Runtime for a consistent environment.
   • CUse MLflow to manually track and retrain models.
   • DUse the standard Databricks Runtime and manually update libraries each time.
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6. 15application · medium

   A data analyst is using AutoML for a binary classification problem. They notice that AutoML is taking a long time to run. What is the primary reason for this?

   • AAutoML is evaluating multiple algorithms and hyperparameter configurations to find the best model.
   • BAutoML is downloading large pre-trained models from the internet.
   • CAutoML is manually reviewing each feature for data quality issues.
   • DAutoML is waiting for user input to select features.
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