To generate images, the application must invoke a foundation model hosted on Amazon Bedrock. Invoking the Amazon Titan Image Generator requires the bedrock:InvokeModel permission. Read-only permissions such as bedrock:Get* do not allow inference execution and are insufficient.

The application also interacts with Amazon Simple Queue Service. To process messages, the application must be able to receive messages from the queue and delete them after successful processing. These actions require sqs:ReceiveMessage and sqs:DeleteMessage.

sqs:GetQueueAttributes alone does not allow message consumption. SageMaker permissions are unrelated because Bedrock is a separate service.

Therefore, permissions to invoke the model and receive/delete SQS messages are required.

Predicting apartment prices is a regression problem, where the target variable is continuous. AWS documentation states that classification metrics such as accuracy, AUC, and F1 score are not appropriate for regression tasks.

Mean Absolute Error (MAE) measures the average absolute difference between predicted values and actual values. MAE is easy to interpret because it is expressed in the same units as the target variable (for example, dollars), making it especially useful for business-facing problems like price prediction.

AWS best practices recommend MAE for evaluating regression models when understanding average prediction error magnitude is important and when robustness to outliers is desired.

Therefore, Option D is the correct and AWS-aligned answer.

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor ' s name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic. Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.

For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.

Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.

AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.

Therefore, Option B is the correct choice.

Amazon SageMaker Clarify is the AWS service used to detect and quantify bias and fairness metrics in ML models. When bias monitoring jobs run, Clarify publishes bias metrics directly to Amazon CloudWatch.

CloudWatch metrics can be visualized using CloudWatch dashboards or integrated into other monitoring tools, making them ideal for real-time or periodic bias reporting.

CloudTrail logs API activity and does not capture ML metrics. EventBridge and SNS are used for event routing and notifications, not metric visualization.

AWS documentation explicitly states that Clarify bias metrics are emitted to Amazon CloudWatch, which is the correct source for dashboards.

Therefore, Option B is the correct and AWS-verified answer.

1️⃣ Simultaneous calls to the endpoint must reach models with the same configuration

Correct strategy: All at once traffic shifting

Why:

“All at once” replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.

2️⃣ The new model must receive only a fraction of the requests for validation before receiving all the traffic

Correct strategy: Canary traffic shifting

Why:

Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.

3️⃣ Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency

Correct strategy: Linear traffic shifting guardrail

Why:

Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.

AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.

Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.

This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.

S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private.

Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account ' s IAM principals to provide the necessary permissions.

Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access.

This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.

Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier Flexible Retrieval ensures cost-effective data retention and storage management while maintaining high performance for recent data retrieval.

Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.

AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.

This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.