Design the ALM Process for AI-powered Business Solutions AB-100 Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   What is a key consideration when designing an ALM process for Microsoft Foundry Agents services?

   • AManually updating agents in production without testing to speed up delivery
   • BAvoiding the use of source control to simplify the deployment process
   • CManaging the versioning and deployment of agent code and configuration consistently
   • DEnsuring that agents are deployed only to production to avoid configuration drift
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2. 2application · medium

   A company uses Dynamics 365 Customer Insights with an AI model that predicts customer churn. The model is updated quarterly. The company wants to ensure that the updated model is tested with a holdout dataset before deployment. What should you do?

   • AUse an Azure Machine Learning pipeline to evaluate the model on a holdout dataset before deploying to Dynamics 365
   • BDeploy the model directly to Dynamics 365 and monitor performance
   • CUse the same dataset for training and validation
   • DManually review the model's performance and then deploy
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4. 3expert · hard

   A team manages Microsoft Foundry Agents that use a shared model registry. The team needs to ensure that only models that pass performance benchmarks are deployed to production, but also needs to allow quick rollback to a previous model if the new model underperforms. What should you implement?

   • AA model registry with automated evaluation gates and a deployment pipeline that supports version rollback
   • BA manual process where data scientists approve each model deployment
   • CA single model version that is updated in place
   • DA monitoring dashboard that alerts on performance degradation
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5. 4application · medium

   A company is developing a custom AI model for fraud detection. The model is retrained monthly with new transaction data. The company wants to ensure that the model is validated against a holdout dataset before it is deployed to production, and that the deployment is automated. What should you implement?

   • AA scheduled job that retrains the model and deploys it without evaluation
   • BA manual process where data scientists review the model and then deploy it
   • CA CI/CD pipeline that trains the model, evaluates it on a holdout dataset, and deploys it if performance thresholds are met
   • DA script that deploys the latest model version to production immediately after training
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6. 5foundation · easy

   Which component is typically versioned as part of a custom AI model's ALM process?

   • AThe physical server hardware used for training
   • BThe company's marketing campaign schedule
   • CThe end-user's device operating system
   • DThe model's training dataset and hyperparameters
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