Explanation

Option C is the correct answer because it meets all the requirements:

1. Uses Amazon Bedrock model invocation logging - CloudWatch Logs can capture model invocation logs from Amazon Bedrock
2. Monitors InputTokenCount and OutputTokenCount metrics - CloudWatch metric filters can extract these specific metrics
3. Detects unusual patterns in tool usage - CloudWatch anomaly detection can identify abnormal patterns
4. Identifies which specific tool integrations cause abnormal token consumption - Metric filters can be configured to extract tool-specific patterns
5. Automatically adjusts thresholds as traffic patterns change - CloudWatch anomaly detection alarms automatically adjust baselines based on historical data

Why other options are incorrect:

Option A: Uses static CloudWatch alarms with fixed thresholds, which doesn't meet the requirement for automatic threshold adjustment as traffic patterns change.

Option B: Uses batch processing with AWS Glue and Amazon Athena, which is good for historical analysis but not for real-time monitoring and automatic threshold adjustment.

Option D: Requires manual threshold updates, which doesn't meet the requirement for automatic threshold adjustment and adds operational overhead.

Key AWS Services Used:

• Amazon CloudWatch Logs: For capturing and storing model invocation logs
• CloudWatch Metric Filters: For extracting specific metrics (InputTokenCount, OutputTokenCount) and tool-specific patterns
• CloudWatch Anomaly Detection: For automatically adjusting baselines and detecting unusual patterns without manual intervention

This solution provides real-time monitoring, adaptive thresholds, and minimal operational overhead while meeting all specified requirements.