Exam Bundle

Health probes confirm readiness without increasing model invocation counts or delaying normal business predictions. This would verify a different operation: Define a lightweight readiness and liveness interface distinct from resource-intensive inference operations. That separate action addresses: A container inference service sends health checks to a path that triggers costly full model predictions and exhausts request capacity during rollouts. It cannot establish the required Granite SaaS correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

A test candidate with a critical slice regression is blocked despite acceptable aggregate F1 performance. This would verify a different operation: Include explicitly approved per-segment acceptance checks and fairness review in the conditional deployment gates. That separate action addresses: A quality gate evaluates only global F1 score while a new candidate violates the acceptable error limit for a protected segment. It cannot establish the required Granite SaaS correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

Both environments deploy without name collisions and their resource tags and model endpoints remain separate. This would verify a different operation: Parameterize account, Region and environment-specific identifiers and maintain isolated deployment stacks and resource references. That separate action addresses: A team's AWS CDK stack uses the same fixed resource names across development and production and fails when a second environment is deployed. It cannot establish the required Granite SaaS correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

Filter EventBridge or S3-trigger inputs to the intended versioned training dataset and apply an idempotent retraining key. It does not directly address the Westhaven Retail case: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The relevant acceptance criterion for this case is: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

Remove embedded credentials and use least-privilege runtime IAM roles or approved secret injection with image scanning. It does not directly address the Westhaven Retail case: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The relevant acceptance criterion for this case is: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

Make the retraining input a named immutable dataset manifest and persist it in the pipeline execution parameters. It does not directly address the Westhaven Retail case: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The relevant acceptance criterion for this case is: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

A replay of the execution reads identical object versions and generates a traceable, comparable training run. This would verify a different operation: Make the retraining input a named immutable dataset manifest and persist it in the pipeline execution parameters. That separate action addresses: A scheduled retraining pipeline uses the latest available dataset rather than the dataset cut recorded in its review ticket. It cannot establish the required Westhaven Retail correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

A risky security-group expansion is blocked during review and a permitted private endpoint change deploys successfully. This would verify a different operation: Require inspected infrastructure change sets and automated network-policy validation before applying the altered endpoint stack. That separate action addresses: A stack update changes an inference endpoint's network settings without showing whether the change broadens inbound access. It cannot establish the required Westhaven Retail correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

The pipeline detects an intentionally incompatible model artifact before endpoint deployment occurs. This would verify a different operation: Add an integration test that starts the intended runtime image with the frozen artifact and invokes a representative inference sample. That separate action addresses: A CI stage validates Python syntax but never tests whether the model container can actually load the published model artifact. It cannot establish the required Westhaven Retail correction for: An internal model deployment needs only private enterprise connectivity but is exposed through an unintended public API path. The decisive acceptance check is instead: An authorized private caller reaches inference while an internet test caller cannot discover or invoke the protected endpoint.

Implement a controlled canary or blue-green rollout with CloudWatch alarm gates and automatic rollback to the approved prior version. It does not directly address the Brookside Manufacturing case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Split the workflow into traceable SageMaker Pipelines or Step Functions stages with typed inputs, outputs and failure handling. It does not directly address the Brookside Manufacturing case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Define a lightweight readiness and liveness interface distinct from resource-intensive inference operations. It does not directly address the Brookside Manufacturing case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node. This would verify a different operation: Use the intended durable artifact storage pattern and reproducible infrastructure definitions rather than node-local attachment assumptions. That separate action addresses: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. It cannot establish the required Brookside Manufacturing correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action. This would verify a different operation: Tune target tracking, cooldown and minimum capacity from observed workload periodicity rather than setting a zero-tolerance threshold. That separate action addresses: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. It cannot establish the required Brookside Manufacturing correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

A test build can publish only its approved artifact and is denied direct modification of the production endpoint. This would verify a different operation: Separate pipeline, build, training and serving roles with narrowly scoped permissions for their specific stages. That separate action addresses: A model project uses one broad IAM role for CodePipeline, training and production inference, enabling build jobs to alter runtime resources. It cannot establish the required Brookside Manufacturing correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Use the intended durable artifact storage pattern and reproducible infrastructure definitions rather than node-local attachment assumptions. It does not directly address the Bluehaven Transit case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Implement a controlled canary or blue-green rollout with CloudWatch alarm gates and automatic rollback to the approved prior version. It does not directly address the Bluehaven Transit case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Configure minimum and maximum capacity with the supported scalable target and test the scaling policy's CloudWatch metric. It does not directly address the Bluehaven Transit case: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The relevant acceptance criterion for this case is: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

A simulated bad candidate triggers the rollback and the restored endpoint returns the documented prior model responses. This would verify a different operation: Implement a controlled canary or blue-green rollout with CloudWatch alarm gates and automatic rollback to the approved prior version. That separate action addresses: A model's automatic deployment increases error rates, but the pipeline has no health-driven rollback condition or retained previous version. It cannot establish the required Bluehaven Transit correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

A deliberately failing test stops the pipeline and no new production model version is published from that commit. This would verify a different operation: Insert automated tests and a quality-gate approval stage before promoting an approved model package to production. That separate action addresses: A model deployment pipeline deploys directly from each developer commit without checking preprocessing unit tests or model evaluation thresholds. It cannot establish the required Bluehaven Transit correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

The pipeline detects an intentionally incompatible model artifact before endpoint deployment occurs. This would verify a different operation: Add an integration test that starts the intended runtime image with the frozen artifact and invokes a representative inference sample. That separate action addresses: A CI stage validates Python syntax but never tests whether the model container can actually load the published model artifact. It cannot establish the required Bluehaven Transit correction for: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. The decisive acceptance check is instead: A representative maximum-size request completes under the chosen platform's supported limits without truncating the response.

Add an integration test that starts the intended runtime image with the frozen artifact and invokes a representative inference sample. It does not directly address the Northgate Telecom case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

Define a lightweight readiness and liveness interface distinct from resource-intensive inference operations. It does not directly address the Northgate Telecom case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

Insert automated tests and a quality-gate approval stage before promoting an approved model package to production. It does not directly address the Northgate Telecom case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

An auditor resolves a production model to the exact source commit that passed the original automated tests. This would verify a different operation: Pin reviewed source commits or release tags and record their identities with immutable model and image artifacts. That separate action addresses: A model endpoint is redeployed from a branch after its source is rebased, making the exact source revision impossible to reconstruct. It cannot establish the required Northgate Telecom correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition. This would verify a different operation: Version the task definition, container image and service deployment configuration as reviewed infrastructure changes. That separate action addresses: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. It cannot establish the required Northgate Telecom correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

The deployed task reports the exact image digest emitted by the reviewed build stage. This would verify a different operation: Pass an immutable ECR image digest as a build artifact to the deployment stage rather than reconstructing a tag. That separate action addresses: A CodeBuild job pushes an inference image to ECR but the subsequent deploy stage uses a different mutable image tag. It cannot establish the required Northgate Telecom correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

Separate pipeline, build, training and serving roles with narrowly scoped permissions for their specific stages. It does not directly address the Harborline Logistics case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

Configure minimum and maximum capacity with the supported scalable target and test the scaling policy's CloudWatch metric. It does not directly address the Harborline Logistics case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

Define a lightweight readiness and liveness interface distinct from resource-intensive inference operations. It does not directly address the Harborline Logistics case: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The relevant acceptance criterion for this case is: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

A destination test role retrieves the artifact and decrypts it while an unrelated account remains denied. This would verify a different operation: Design the artifact sharing, replication and KMS policies explicitly for each trusted cross-account stage. That separate action addresses: A cross-account promotion pipeline copies model artifacts but loses the KMS permissions required for the destination account to load them. It cannot establish the required Harborline Logistics correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

The runtime can read its designated artifacts and log streams but cannot modify unrelated CloudFormation stacks. This would verify a different operation: Separate infrastructure deployment roles from runtime execution roles with narrowly scoped resource access policies. That separate action addresses: A deployment template grants one IAM role both model-serving permissions and broad stack-administration privileges unrelated to inference. It cannot establish the required Harborline Logistics correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.

A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node. This would verify a different operation: Use the intended durable artifact storage pattern and reproducible infrastructure definitions rather than node-local attachment assumptions. That separate action addresses: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. It cannot establish the required Harborline Logistics correction for: A platform team deploys a custom PyTorch inference container without the correct SageMaker-compatible serving endpoint contract. The decisive acceptance check is instead: The endpoint becomes healthy and returns a valid prediction for a documented JSON request through the SageMaker invocation API.