模擬試験 1

Tagging every model package would add metadata to more than 1,200 versions and create ongoing maintenance. It does not provide the purpose-built domain grouping layer for existing model groups. Trap: Tags sound flexible for discovery, but this scenario asks to organize existing groups at scale without editing package-level assets.

Creating new domain model groups and moving models would change current model group memberships, which the prompt explicitly forbids. Trap: Domain model groups sound clean, but they require restructuring the registry instead of adding an organization layer.

SageMaker ML Lineage Tracking records relationships among data, jobs, models, and deployments. It does not automatically infer business domains or organize Model Registry groups into domain buckets. Trap: Lineage is useful for traceability, but traceability is not the same as business-domain discoverability.

Image classification predicts labels for the whole image and does not return coordinates for each fruit. Trap: It sounds plausible because the data is images, but the required output is localization.

XGBoost is mainly for tabular classification or regression and does not directly process raw images into bounding boxes. Trap: It is a common SageMaker built-in algorithm, but the task is computer vision localization.

k-NN works on feature vectors for nearest-neighbor classification or regression; it is not a bounding-box detector for raw imagery. Trap: It can classify examples, but it does not produce per-object spatial coordinates.

Increasing eta makes each boosting step larger and usually fits the training data more aggressively, which is the wrong direction when training metrics are already near perfect. Trap: Learning rate sounds like a training improvement lever, but the symptom is high variance, not under-training.

Removing features could sometimes reduce overfitting, but the question explicitly says not to change the feature space. Trap: Feature pruning is a plausible data-preparation response to noisy predictors, but it violates the stated constraint.

Increasing max_depth makes each tree more complex and gives XGBoost even more ability to memorize the training week. Trap: More capacity can help underfitting, but this model is already overfitting badly.

Training and deploying custom SageMaker models directly violates the no-training requirement and adds the most engineering effort. Trap: SageMaker can build custom ML systems, but the question asks for the fastest managed AI solution.

Rekognition analyzes images and video content but is not the speech-to-text service for podcasts and conference recordings. Trap: It mentions Bedrock and Translate, but the media conversion step is the wrong AWS capability.

Comprehend works on text rather than transcribing audio or video, and Stable Diffusion is an image generation model, not a summarization model. Trap: The option includes language services, but both the media ingestion and generative model choice are misaligned.

Accuracy counts correct class predictions, but the target is a continuous resale price with no natural correct/incorrect class label. Trap: It is a common metric name, but it only fits classification unless the problem is reframed into bins.

AUC evaluates a classifier's ranking across thresholds and does not report dollar error for a numeric prediction. Trap: AUC sounds robust and threshold-aware, but the scenario is not binary classification.

F1 combines precision and recall for classification outcomes, so it cannot express average resale-price error in USD. Trap: F1 is useful for imbalanced classes, but there are no classes in this regression target.

SageMaker Debugger helps inspect training or model behavior, but it does not mirror live endpoint requests to a candidate model for no-impact production comparison. Trap: Debugger sounds like evaluation tooling, but this is an inference deployment pattern.

All-at-once blue/green shifting would move production decisions to the new version immediately, violating the no-impact requirement. Trap: Blue/green is valid for release cutovers, not for invisible 24-hour evaluation.

Canary shifting still sends some users to the new model for actual responses, so production decisions and latency can change. Trap: A 10% canary seems safer than all-at-once, but it still affects real users.

The question already says the users are in the same SageMaker domain, which is the sharing context. Different domains would create a separate boundary and is not a requirement. Trap: It confuses isolation with shareability; Canvas sharing is not enabled by separating users into different domains.

AWS Marketplace is for distributing products externally under marketplace terms. It is unnecessary and unrelated to internal sharing with a Canvas user in the same SageMaker domain. Trap: Publishing sounds like sharing, but this is not a commercial or cross-customer distribution scenario.

A real-time endpoint is a deployment option for synchronous inference, not a prerequisite for Canvas model discovery or no-code evaluation. The requirement is to share the model, not host it for production traffic. Trap: Endpoint deployment is a later inference lifecycle step and can be confused with making a model available.

Step Functions can coordinate Batch and SageMaker, but the question explicitly says not to introduce state machines. Trap: It is a strong orchestration service, so it is tempting, but it violates a hard constraint.

SageMaker Processing inputs point to data sources such as S3; they do not reference AWS Batch job ARNs as consumable artifacts. Trap: It mentions SageMaker Pipelines, but it confuses job metadata with data inputs.

A callback step would require an external actor to start Batch and call back to the pipeline, adding moving parts for an already-running Batch workflow. Trap: Callback sounds like waiting for external work, but it is not the lowest-ops event trigger for Batch success.

IAM groups cannot be associated directly with SageMaker notebook instances; the notebook runtime uses an execution role. Trap: Groups sound centralized, but they manage user permissions rather than service execution permissions.

Using one IAM user with AdministratorAccess creates long-term credential risk and violates the least-privilege requirement. Trap: One user would be centralized, but it is the wrong identity type and grants far too much access.

IAM groups still cannot be attached to notebook instances, and AdministratorAccess is broader than required. Trap: Combining groups and roles sounds flexible, but it does not match how SageMaker execution roles work.