The agent responded. Did the work finish?
A support agent issues a credit, times out, and tries again. The second call looks like a sensible recovery—until the customer receives two credits. A better prompt may not solve the problem because the missing control sits in the workflow around the model.
This is the kind of distinction the practice tests ask you to make. You will work through agent design as a connected system: what the agent can see, which tools it can use, how work is recorded, and how a failed or uncertain run can be recovered. The point is to make your diagnosis more precise than ‘the agent went wrong.’
From orchestration to day-to-day operation
The questions cover control planes and gateways, including identity, routing, quotas, and policy. They also examine the machinery around an agent: state, memory, approvals, checkpoints, retries, and side effects. Multi-agent coordination includes supervisors, workers, subagents, and graph-based workflows, with attention to whether the extra coordination actually helps.
Development topics connect specifications and tools to evaluations, release gates, and rollback. Operational topics bring in traces, reliability objectives, security, support, cost, and ownership. That combination matters because a capable agent can still be difficult to release safely or expensive to keep running.
Use the explanations to identify the layer that owns the fix. A missing source is not the same as a missing permission. A model-quality problem is not the same as an unreliable tool. Adding another agent should answer a demonstrated need, not become the default response to complexity.
Practice format
Six advanced tests built around production-agent decisions.
You get 6 practice tests, with 75 questions in each test (450 questions in total).
Each test gives you 120 minutes.
The course language is English, and the course level is Advanced. The configured practice-test pass mark is 75%.
Is this the right fit?
Best suited to engineers, architects, and platform or product professionals moving beyond a prototype. Familiarity with LLMs, RAG, and tool calling will help you get more from the scenarios. This is not a first introduction to AI or a substitute for building and testing a working implementation.
A sample of the reasoning
A support agent is authorized to issue credits up to $50. A tool call times out, and an automatic retry creates a second credit. Which change most directly addresses this failure?
Use a stronger model to improve the agent's reasoning before retries.
Use an idempotency key and check the durable result before retrying.
Increase the credit limit so the agent has more flexibility during recovery.
Add detailed logs while keeping the existing retry behavior unchanged.
Best answer: B. B addresses duplicate effects, rather than the quality of the agent's explanation. The receiving system must honor the idempotency mechanism, and the workflow needs a way to check what actually completed. A stronger model or a larger limit does not provide that protection. Logging helps investigation, but logging alone leaves the duplicate-credit path intact.
Illustrative public-page example, reworded for this edition; not a live examination item.
Follow a failed run, not just the score
When an answer surprises you, trace the request through context, model, tool, state, and confirmation. Name the first point that stopped matching the intended behavior. Then compare the proposed repair with the nearest alternative. Use a fresh test after revisiting that failure pattern; immediately repeating the same wording is a poor way to discover whether your diagnosis has improved.
Related Field Notes
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