AIP-C01 practice question: Agent and tool observability (AgentCore Observability, tracing)

Operational Efficiency and Optimization for GenAI Applications A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

An airline's rebooking team uses an AI agent hosted on Amazon Bedrock AgentCore. For each disruption, the agent calls a model several times and runs tools that check seats, fares and passenger records. Some runs take over a minute, and the team cannot see why.

The team needs to see the time spent in each model call and each tool call within one run, and to see which step returned an error. The agent is built with an open-source framework, and the team wants a standard format that other tools can read.

The telemetry must cover every agent run in production, use a vendor-neutral format, and appear in CloudWatch for the on-call engineers without the team writing its own tracing system.

Which approach meets these requirements?

Choose one answer.

A. Add print statements with timestamps to each tool function, write them to a file on the runtime host, and search that file with a script whenever a slow run is reported.
B. Turn on AgentCore Observability, which emits OpenTelemetry traces with a span for each model and tool call, and view the traces and their metrics in CloudWatch dashboards.
C. Rely on Amazon Bedrock model invocation logs, which record every call the agent makes, including the time that the agent's tools took to run inside its own code.
D. Ask Amazon Q Developer to review the agent's source code for slow loops and blocking calls, and fix the issues it reports before each new release of the agent.

Show the answer and explanation

Answer: B

A. Wrong: Print statements show timing only for the code that was instrumented, and log files on runtime hosts are hard to correlate across a run. Searching them after each report also gives the on-call engineers no live view.
B. Right: OpenTelemetry is a standard format that other tools can read, and the spans show time and errors for each model and tool call within one run. The traces appear in CloudWatch without a custom tracing system. Learn more: Observe your agent applications on Amazon Bedrock AgentCore Observability (opens in a new tab)
C. Wrong: Invocation logs record the model calls, but tools run inside the agent's code, so their time and errors are not captured there. The option leaves the tool steps invisible, which is the gap the team has. Learn more: Monitor model invocation using CloudWatch Logs and Amazon S3 (opens in a new tab)
D. Wrong: A code review can suggest likely causes, but it cannot show the actual timing of a live run or which step failed in production. The option does not provide the per-run telemetry the team needs.

Explanation

AgentCore Observability emits OpenTelemetry traces with spans for each model call and tool call, so a slow run can be traced step by step in CloudWatch. Print statements and invocation logs miss tool execution, and a code review cannot show live timing or the failing step.

AIP-C01 practice question: Agent and tool observability (AgentCore Observability, tracing)

Explanation

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