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| Section | Weight | Objectives |
|---|---|---|
| AI Safety, Security, and Governance | 20% | - Secure generative AI systems
|
| Implementation and Integration | 26% | - Integrate foundation models into applications and workflows
|
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Optimize performance and cost
|
| Testing, Validation, and Troubleshooting | 11% | - Troubleshoot and resolve issues
|
| Foundation Model Integration, Data Management, and Compliance | 31% | - Ensure compliance and responsible AI practices
|
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問題 #74
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models.
The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?
答案：D
解題說明：
Option C best meets the requirements by combining native Amazon Bedrock logging with adaptive monitoring and minimal operational overhead. Amazon Bedrock model invocation logging can be sent directly to CloudWatch Logs, where detailed fields such as InputTokenCount, OutputTokenCount, and tool invocation metadata are captured for each request.
CloudWatch metric filters allow extraction of structured metrics from logs, including tool-specific token consumption patterns. By defining filters per tool integration, the company can isolate which tools are responsible for increased token usage without building custom log-processing pipelines.
CloudWatch anomaly detection provides automatic baseline modeling and dynamic thresholds based on historical traffic patterns. Unlike static alarms, anomaly detection adapts as usage evolves, making it ideal for applications with changing workloads or seasonal usage patterns. This directly satisfies the requirement to automatically adjust thresholds as traffic patterns change.
When abnormal token consumption occurs, anomaly detection alarms trigger immediately, enabling rapid investigation and remediation. Because this solution uses fully managed AWS services without custom analytics jobs or manual threshold tuning, it significantly reduces operational effort.
Option A fails to adapt to changing patterns. Option B introduces batch analysis and delayed insights. Option D requires manual intervention and custom code, increasing maintenance burden.
Therefore, Option C provides the most scalable, adaptive, and low-maintenance solution for monitoring and controlling token consumption in Amazon Bedrock-based applications.
問題 #75
An insurance company uses existing Amazon SageMaker AI infrastructure to support a web-based application that allows customers to predict what their insurance premiums will be. The company stores customer data that is used to train the SageMaker AI model in an Amazon S3 bucket. The dataset is growing rapidly. The company wants a solution to continuously re-train the model. The solution must automatically re-train and re- deploy the model to the application when an employee uploads a new customer data file to the S3 bucket.
Which solution will meet these requirements?
答案：D
解題說明：
Option D is the best fit because it implements a reliable event-driven MLOps workflow that automates retraining and redeployment with clear orchestration, auditability, and production-grade error handling. The requirement is explicit: whenever a new file is uploaded to Amazon S3, the system must retrain and then redeploy the model used by a web application. A common AWS pattern is to use an S3 event notification to trigger an AWS Lambda function, which then starts a controlled workflow. In option D, Lambda serves as the event handler that reacts immediately to the S3 upload event and passes the necessary context (bucket, object key, dataset version) into an AWS Step Functions Standard state machine.
Step Functions Standard is appropriate for model retraining pipelines because training and deployment steps can be long-running and benefit from durable state, retries, and failure handling. It provides execution history, making it easier to troubleshoot why a particular retraining run failed and to prove which dataset version produced which model version. This operational visibility is critical when the dataset is "growing rapidly" and retraining is frequent.
Within the workflow, Amazon SageMaker Pipelines is the right service to run the ML lifecycle stages in a repeatable way: data processing (if needed), training, evaluation/quality checks, model registration, and deployment to an endpoint used by the application. SageMaker Pipelines is purpose-built for CI/CD-style ML, supporting automated redeployments when a new approved model artifact is produced. By calling a pipeline execution from Step Functions, the company can add governance gates (for example, only deploy if evaluation metrics meet thresholds), and can apply consistent rollback and notification steps when deployment fails.
The other options are weaker: A confuses inference with retraining and does not provide deployment orchestration. B adds unnecessary webhook complexity and describes an awkward event bus configuration. C introduces Autopilot/Data Wrangler, which may be useful but adds extra moving parts and is not required to meet the trigger-and-redeploy requirement.
問題 #76
A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted. Which solution will meet these requirements?
答案：C
解題說明：
Amazon Q Developer Pro provides a built-in administrator dashboard designed specifically for organizational observability. This dashboard provides native visibility into user-level metrics across the entire AWS Organization, allowing administrators to identify active vs. underused subscriptions and recognize power users. Crucially, it tracks high-level usage patterns, including code acceptance metrics (such as lines of code generated and accepted), which is a key requirement for measuring ROI and identifying training needs. Using the built-in dashboard provides the necessary insights with the least operational overhead, as it does not require building custom data pipelines (Option C) or complex log processing architectures (Option D).
問題 #77
A global financial services company hosts a fraud-alert system that uses an Amazon Bedrock foundation model (FM) to generate explanations for suspicious transactions. The company processes regulated financial data across three geographic areas. The system must maintain consistent responsiveness globally, support multi-Region failover, and provide full observability for audit and compliance teams.
Load testing shows that the FM's total inference time cannot be reduced. The company cannot increase its inference costs, change the FM, modify token counts, or provision additional compute capacity. Users report that the UI performs slowly because it waits for the complete model response before it shows any content.
The company must improve perceived responsiveness during peak periods, when the system can receive
10,000-15,000 concurrent requests. The solution must maintain multi-Region resiliency and full monitoring visibility.
Which solution will meet these requirements?
答案：D
解題說明：
Option A is correct because the problem is not total model runtime; the problem is perceived UI responsiveness. AWS documentation states that the Amazon Bedrock InvokeModelWithResponseStream API returns the model response as a stream. AWS also explains that streaming allows an application to access responses in chunks without waiting for the entire result. This lets the frontend display generated content as tokens or chunks arrive, improving the user experience even when the total inference duration remains unchanged.
This option also preserves the stated constraints. It does not require changing the foundation model, increasing token counts, provisioning extra compute, or reducing the model's actual inference time. Instead, it changes how the response is delivered to the user. This is exactly the right pattern when users perceive slowness because the interface waits for the full completion before rendering anything. AWS examples for Bedrock streaming show applications processing response streams in real time, which supports interactive applications during high-concurrency periods.
The monitoring portion of option A is also aligned with AWS guidance. Amazon Bedrock runtime publishes CloudWatch metrics for invocation volume, latency, token consumption, error rates, and model invocation logging delivery. CloudWatch GenAI observability can track invocation count, token usage, and errors using built-in views. This supports audit and compliance visibility across Regions.
Option B adds caching of partially processed fraud explanations, which can be risky for regulated transaction- specific explanations and does not solve the UI issue for unique requests. Option C changes inputs by condensing them, which violates the constraint not to modify token counts or inputs. Option D focuses on Lambda concurrency and log retention, which does not address waiting for the full model response.
Therefore, response streaming with CloudWatch and tracing is the correct solution.
問題 #78
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?
答案：C
解題說明：
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.
問題 #79
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IT專業技術認證是進入IT行業的“敲門磚”。由國際著名IT企業頒發的職業證書，證明了你具有某種專業IT技能，為國際承認並通用。這些國際著名 IT企業為：Microsoft、Oracle、Cisco、Amazon、IBM、Oracle等。AIP-C01 考試就是其中一個流行的 Amazon 認證。許多考生對這門考試沒有什麼信心，其實，AIP-C01 最新的擬真試題是用最快和最聰明的的方式來傳遞您的考試，並幫助您獲得 Amazon AIP-C01 認證。
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