CCDV-F Agents and the Agent SDK Practice Question
When an agent is asked to perform a complex task, which approach minimizes latency while ensuring accuracy?
⚠ Common exam trap
Candidates frequently choose a single massive prompt to solve complex tasks, believing it saves time, rather than breaking the problem down into chained agent actions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decomposing the task into smaller, chained agent actions.
Breaking down a complex task into smaller, sequential tool-use steps allows the model to reason incrementally. This approach reduces the load per turn, makes errors easier to isolate, and improves overall accuracy by allowing the model to process feedback at each stage. While it increases the total number of turns, it is often faster than forcing a single, massive inference that may fail entirely.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Requesting the model to solve the entire problem in one turn.
Why it's wrong here
Single-turn solutions for complex tasks often hit context or reasoning bottlenecks. They are prone to failure and difficult to debug, as the model lacks intermediate feedback loops that would allow it to self-correct during the process of solving the problem.
- ✓
Decomposing the task into smaller, chained agent actions.
Why this is correct
Task decomposition allows the model to verify its progress at each step. By chaining actions, the agent manages complexity effectively, reducing the likelihood of catastrophic errors while making the entire process more transparent and easier to monitor for performance.
- ✗
Using the maximum possible context window for every request.
Why it's wrong here
Using a maximum context window is inefficient and expensive. It increases latency because the model must process a larger amount of data in every request, even if that data is irrelevant to the current step in the task.
- ✗
Adding 'think step-by-step' to the final response request.
Why it's wrong here
While chain-of-thought is useful, it is not a substitute for architectural task decomposition. Relying on internal reasoning for complex multi-step tasks is far less reliable than explicitly executing discrete tools for each logical phase of the problem.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
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