Design and implement an MLOps infrastructure

AI-300 · 38 questions

• A city IT office is launching a permits forecasting pilot and wants data scientists to share one governed Azure Machine Learning project boundary instead of scattering experiments across personal subscriptions. What should they create first?
• A county public-health analytics team must place a new Azure Machine Learning workspace so it inherits the same billing, policy, and network controls used for other HIPAA-scoped civic workloads. Where should the workspace be created?
• A water utility keeps training datasets in an Azure Blob container and wants Azure Machine Learning jobs to read that data in place. What should the MLOps engineer register?
• A transit authority stores ridership CSVs in Azure Data Lake Storage Gen2 and must expose them to Azure Machine Learning training jobs. Which datastore approach fits best?
• A housing authority needs overnight AutoML jobs for waitlist forecasting to run on scalable CPU capacity without leaving expensive compute online all day. Which compute choice best matches that need?
• A fire-district pilot needs GPU capacity for deep-learning smoke-detection experiments but only CPU for tabular permit-risk models. How should compute targets be matched?
• A city clerk's office wants data scientists to submit jobs to a shared Azure Machine Learning compute cluster but must prevent them from deleting the workspace. Which access approach is appropriate?
• A county courts IT team needs CI pipelines to submit Azure Machine Learning pipeline jobs without interactive user login. What identity pattern should they use?
• A parks department wants seasonal interns to view production scoring metrics in Azure Machine Learning without altering datastores. What should the MLOps lead configure?
• A 311 analytics unit finds Azure Machine Learning jobs failing after a storage account key rotation. What should the team update?
• A municipal open-data team needs Azure Machine Learning jobs to pull structured parcel attributes from Azure SQL for feature engineering. What should they configure?
• A school-district facilities team needs interactive notebook exploration of energy-usage models before any scheduled multi-node training. Which compute target fits that interactive work?
• A library consortium requires Azure Machine Learning workspace diagnostic logs to support municipal audit reviews. Where should those logs be sent?
• A permits office wants reproducible Azure Machine Learning training runs that always pin the same historical applications table. What asset should they create?
• A public-health lab finished cleaning lab-results data and must promote exploration v1 to a production training input without renaming folders by hand. What practice should they use?
• A transit ML team wants every ridership training job to use the same pinned scikit-learn and pandas versions. What should they define once and reuse?
• A water-quality model's production scoring fails because a preprocessing package that existed in training is missing from the scoring environment. What should the team fix?
• A city fraud-detection effort needs the same feature-engineering logic reused across several Azure Machine Learning pipelines. What asset type should they publish?
• A county assessor team validated a data-prep step and wants other departments to compose it into their own Azure Machine Learning pipelines. What should they do?
• Two city departments need to share a golden environment image across separate Azure Machine Learning workspaces without emailing Dockerfiles. Which capability should they use?
• A regional emergency-management partnership wants mutual-aid forecasting teams in different Azure Machine Learning workspaces to consume the same registered model-component catalog. What should they establish?
• A housing authority must keep PII-bearing resident files from being treated like public open-data assets in Azure Machine Learning. What discipline should they apply to data assets?
• A municipal climate team must patch an Azure Machine Learning environment after a dependency CVE while keeping builds reproducible. What should they do?
• A courts analytics group has a large ETL process that is hard to debug as one monolithic Azure Machine Learning step. How should they improve maintainability?
• A city MLOps lead has a vetted featurizer in a sandbox Azure Machine Learning registry and must make it available to production workspaces. What is the correct promotion action?
• A city platform team must deploy identical Azure Machine Learning workspaces to staging and production without repeating portal clicks. Which approach best meets that requirement?
• A county DevOps engineer must create Azure Machine Learning compute and datastores alongside a new workspace as part of an automated pipeline. Which approach best fits that goal?
• A transit authority needs Azure Machine Learning training jobs to pull code from a private civic GitHub repository with secure access. What should the MLOps team configure?
• A public-works MLOps workflow should provision a new Azure Machine Learning experiment compute cluster automatically when a deploy workflow runs. Which capability should they use?
• A health department requires that its Azure Machine Learning workspace accept traffic only from the municipal private network, not the public internet. What should the team implement?
• A city ML project must keep training scripts, pipeline YAML, and environment files under version control with branch protection before production deploys. Which practice should the team adopt?
• A parks analytics team must review infrastructure changes that alter Azure Machine Learning workspace private-endpoint settings before those changes reach production. What is the best control?
• A clerk's office forbids long-lived personal access tokens for Azure Machine Learning and GitHub integration and prefers a short-lived federated identity pattern. What should they configure?
• A school-district MLOps runbook must apply Bicep to create a workspace and then register default environments in that workspace during the same automation. Which approach best completes both steps?
• A water utility security review finds private endpoints on an Azure Machine Learning workspace, yet the workspace still allows public network access. What must the team enforce to meet the review?
• A multi-agency grant project needs experiment pipeline definitions kept separate from production definitions, with required reviews before production changes. Which source-control practice should they use?
• After a Bicep deploy, a city SRE wants CI to confirm the Azure Machine Learning workspace managed identity can reach the registered datastore before training jobs are allowed. What should CI include?
• A county wants ephemeral Azure Machine Learning experiment workspaces removed automatically after short-lived grant pilots to control cost. Which approach best supports that lifecycle?