Exams / AIP-C01 / Study guide / Developer productivity and troubleshooting tools (Kiro, Amazon Q Developer, Logs Insights)
Developer productivity and troubleshooting tools (Kiro, Amazon Q Developer, Logs Insights): AIP-C01 study notes and practice questions
What to know about Developer productivity and troubleshooting tools (Kiro, Amazon Q Developer, Logs Insights) for the AIP-C01 exam, from the Implementation and Integration domain (26% of the exam guide), then 1 free question to test it. Part of the AIP-C01 study guide.
AIP-C01 asks which tool speeds up building, testing or debugging a generative AI feature. The answer is usually the tool that matches the task, not the most general one.
Spec-driven development with Kiro
Kiro is AWS’s agentic IDE. Specs turn a feature request into requirements, design and task documents that the agent works through. Steering files hold project rules, and hooks run actions on events such as a file save. Use Kiro when a generative AI feature needs a clear spec and repeatable tasks inside the IDE. Amazon Q Developer IDE plugins reach end of support on April 30, 2027, so choose Kiro for new IDE work.
Amazon Q Developer
Q Developer answers questions about AWS services in the console and the CLI, helps diagnose console errors, and assists with code generation, refactoring and unit tests. It remains a sound choice for console and CLI help. Use it to generate unit tests for prompt-handling code, such as parsers and input validators, and run those tests in the pipeline.
CloudWatch Logs Insights
Logs Insights queries invocation logs to find error patterns, latency percentiles, throttling counts and token usage by model or application. Use it when many requests failed and you need to see which error code or input pattern appears most. It can only show prompts and responses when model invocation logging is turned on, which is off by default. Mask sensitive fields in the log group, because prompts often contain personal data.
Tracing model calls
X-Ray and AWS Distro for OpenTelemetry trace a request across API Gateway, Lambda and model calls, showing which step added latency or failed. Instrument new code with OpenTelemetry. The X-Ray service remains in use, but the X-Ray SDKs are in maintenance.
How to choose
Scenario wording
Choose
“Turn a feature idea into specs and tasks in an IDE”
Kiro specs
“Generate tests for prompt-handling code”
Amazon Q Developer or Kiro
“Ask how to fix a CLI or console error”
Amazon Q Developer
“Find which error code spikes across failed invocations”
CloudWatch Logs Insights
“See latency for each step of an agent request”
X-Ray or OpenTelemetry traces
“Who called InvokeModel, and when”
AWS CloudTrail
Common traps
Expecting prompt text in Logs Insights when invocation logging is off.
Using CloudTrail to read prompts or responses. CloudTrail records API calls and the caller, not the content.
New X-Ray SDK instrumentation when OpenTelemetry is the supported path.
Choosing Q Developer IDE plugins for a new IDE project, since their support ends in 2027.
Logging full prompts with personal data and no masking.
A five-person startup is building a feature that extracts clauses from contracts with a model on Amazon Bedrock. The developers describe changes in chat, and the code has drifted from what the product owner agreed to. Reviewers now ask for written acceptance criteria before any code is merged.
The team needs three things. Requirements with acceptance criteria and a task list should be reviewed before code is written, the team's coding standards should apply in every developer's AI coding session, and the work should happen in Kiro, the team's chosen IDE.
Which TWO actions meet these requirements? (Choose two.)
Choose two answers.
AA. Use Kiro's spec-driven workflow, writing requirements with acceptance criteria, approving a design document, and then running the task list that is generated from the approved spec.
BB. Paste each product request into a general chat assistant, ask it to write the whole feature in one prompt, and review the generated diff after the code is written.
CC. Keep the requirements in a shared wiki page and link it in each pull request description, while every developer works from their own unshared prompt history.
DD. Store the team's coding standards as steering files in the repository in Kiro, so that every developer's Kiro session loads the same conventions automatically.
EE. Add a CloudWatch Logs Insights query that counts the merged pull requests each week, so that the team can see how many changes were reviewed.
Show the answer and explanation
Answers: A, D
A. Right: Kiro's spec workflow puts the requirements, design and tasks in files that the team reviews before coding starts. Each task traces back to an acceptance criterion, which meets the review requirement. Learn more: Kiro documentation (opens in a new tab)
B. Wrong: Reviewing after the code is written is the drift the team wants to stop. A single prompt also leaves no agreed requirements or task list for reviewers to approve first.
C. Wrong: A wiki link helps reviewers, but the requirements are not reviewed before coding, and the standards depend on each developer's own prompts. Both requirements are missed.
D. Right: Steering files live with the code, and Kiro loads them into every session in the project. This applies the same standards to each developer's work without anyone repeating them in prompts. Learn more: Kiro documentation (opens in a new tab)
E. Wrong: A query reports on merges after they happen and does not set requirements or standards for the work. It does not meet either the review-before-coding or the standards requirement.
Explanation
Kiro's spec workflow makes the team review requirements, design and tasks before code is written, with acceptance criteria that tie back to each task. Steering files in the repository give every developer's Kiro session the same standards. Chat prompts, wiki links and log queries leave the review or the standards requirement unmet.
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