Exam Bundle

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 Harborview Insurance correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

A staged rollback restores the approved prior model version and representative requests return to baseline behavior. This would verify a different operation: Maintain versioned model artifacts and use a controlled deployment strategy with retained previous endpoint configuration. That separate action addresses: A production team deploys an updated fraud model by replacing the only model artifact, leaving no usable rollback option. It cannot establish the required Harborview Insurance correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

A deliberately underperforming candidate remains unapproved and a passing candidate records its evaluation evidence. This would verify a different operation: Add evaluation and conditional model-quality steps linked to the model registry approval process. That separate action addresses: A SageMaker Pipelines workflow trains and deploys without recording evaluation output, so every candidate is automatically approved. It cannot establish the required Harborview Insurance correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Use a supported SageMaker inference pipeline or reviewed multi-container design with compatible input and output contracts. It does not directly address the Oakridge Public Services case: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The relevant acceptance criterion for this case is: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Evaluate SageMaker multi-model endpoints where model memory and isolation requirements permit compatible shared hosting. It does not directly address the Oakridge Public Services case: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The relevant acceptance criterion for this case is: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Use a governed endpoint-variant routing or other supported traffic allocation design with versioned model identifiers. It does not directly address the Oakridge Public Services case: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The relevant acceptance criterion for this case is: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Only an approved data manifest triggers retraining and repeated notifications do not launch duplicate experiments. This would verify a different operation: Filter EventBridge or S3-trigger inputs to the intended versioned training dataset and apply an idempotent retraining key. That separate action addresses: An automated model retraining workflow starts every time any S3 object changes, including log and metadata files unrelated to training. It cannot establish the required Oakridge Public Services correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

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 Oakridge Public Services correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Two identical trigger messages resolve to the expected single logical model update without competing untracked releases. This would verify a different operation: Include stable dataset IDs and idempotency controls in the orchestration design, preserving run lineage and concurrency policies. That separate action addresses: A retraining pipeline can be launched twice for the same input data, creating competing model versions with indistinguishable metadata. It cannot establish the required Oakridge Public Services correction for: A model deployment relies on one manually created EBS volume that a replacement task cannot attach after failure. The decisive acceptance check is instead: A replacement task starts from the approved artifact version without needing an untracked disk originally attached to another node.

Make the retraining input a named immutable dataset manifest and persist it in the pipeline execution parameters. It does not directly address the Meadowbrook Finance case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Evaluate SageMaker multi-model endpoints where model memory and isolation requirements permit compatible shared hosting. It does not directly address the Meadowbrook Finance case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Compare supported CPU, GPU or inference-optimized instances using matched representative throughput and latency tests. It does not directly address the Meadowbrook Finance case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

An oversized test input is queued, processed and retrieved asynchronously without the client's synchronous timeout. This would verify a different operation: Choose SageMaker asynchronous inference or a supported job-based flow with input-output object locations and completion handling. That separate action addresses: An image analysis API accepts infrequent large payloads with long processing times but times out when called synchronously through a normal endpoint. It cannot establish the required Meadowbrook Finance correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

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 Meadowbrook Finance correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

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 Meadowbrook Finance correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Add evaluation and conditional model-quality steps linked to the model registry approval process. It does not directly address the Redstone Aviation case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Benchmark CPU or suitable serverless hosting and select the deployment offering that meets utilization and latency requirements. It does not directly address the Redstone Aviation case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Compare supported CPU, GPU or inference-optimized instances using matched representative throughput and latency tests. It does not directly address the Redstone Aviation case: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The relevant acceptance criterion for this case is: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

A staged rollback restores the approved prior model version and representative requests return to baseline behavior. This would verify a different operation: Maintain versioned model artifacts and use a controlled deployment strategy with retained previous endpoint configuration. That separate action addresses: A production team deploys an updated fraud model by replacing the only model artifact, leaving no usable rollback option. It cannot establish the required Redstone Aviation correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

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 Redstone Aviation correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

A representative maximum-size request completes under the chosen platform's supported limits without truncating the response. This would verify a different operation: Compare Lambda, SageMaker asynchronous inference or container hosting based on payload size, memory and duration constraints. That separate action addresses: An inference service needs quick per-document processing with substantial response payloads but a proposed Lambda implementation exceeds its documented resource or duration limits. It cannot establish the required Redstone Aviation correction for: An Amazon ECS model-serving service cannot be updated safely because its task definition and container port are modified directly on running hosts. The decisive acceptance check is instead: A controlled ECS rollout replaces tasks with the intended version and returns successful health checks throughout transition.

Require the intended registered model package approval state and enforce a deployment role boundary at the promotion stage. It does not directly address the Highland Education case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

Evaluate SageMaker multi-model endpoints where model memory and isolation requirements permit compatible shared hosting. It does not directly address the Highland Education case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

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 Highland Education case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

The selected instance family meets throughput and latency targets with a documented cost-per-inference measurement. This would verify a different operation: Compare supported CPU, GPU or inference-optimized instances using matched representative throughput and latency tests. That separate action addresses: A high-volume endpoint runs on small CPUs despite a documented model benchmark showing matrix inference benefits from accelerator hardware. It cannot establish the required Highland Education correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

A controlled deployment must pass endpoint health, representative inference and alarm checks to reach the completed state. This would verify a different operation: Add post-deployment smoke tests and monitored acceptance gates before the production release is marked successful. That separate action addresses: A team declares its pipeline finished when model training succeeds, though deployment smoke tests and runtime metrics are never observed. It cannot establish the required Highland Education correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

A representative live test receives each decision within the checkout deadline without waiting for a batch schedule. This would verify a different operation: Deploy an appropriately scaled low-latency real-time inference endpoint and route transaction requests through the authorized path. That separate action addresses: A web fraud detector requires low-latency inference for each checkout, but the proposed architecture waits for the next hourly batch transformation. It cannot establish the required Highland Education correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

Add evaluation and conditional model-quality steps linked to the model registry approval process. It does not directly address the Maplewood Schools case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

Add post-deployment smoke tests and monitored acceptance gates before the production release is marked successful. It does not directly address the Maplewood Schools case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

Separate pipeline, build, training and serving roles with narrowly scoped permissions for their specific stages. It does not directly address the Maplewood Schools case: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The relevant acceptance criterion for this case is: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

A controlled deployment must pass endpoint health, representative inference and alarm checks to reach the completed state. This would verify a different operation: Add post-deployment smoke tests and monitored acceptance gates before the production release is marked successful. That separate action addresses: A team declares its pipeline finished when model training succeeds, though deployment smoke tests and runtime metrics are never observed. It cannot establish the required Maplewood Schools correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

An oversized test input is queued, processed and retrieved asynchronously without the client's synchronous timeout. This would verify a different operation: Choose SageMaker asynchronous inference or a supported job-based flow with input-output object locations and completion handling. That separate action addresses: An image analysis API accepts infrequent large payloads with long processing times but times out when called synchronously through a normal endpoint. It cannot establish the required Maplewood Schools correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.

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 Maplewood Schools correction for: An auto-scaled inference endpoint scales out during every brief request spike and incurs unnecessary instance churn and warm-up overhead. The decisive acceptance check is instead: A replay of transient spikes avoids oscillation while sustained load still triggers the required scale-out action.