CCAR-P Practice Question: Stakeholder Communication and Lifecycle Management
A retail client's marketing director tells you the Claude-based product description generator is 'too slow' and wants it fixed by the end of the week. Before committing engineering effort, what should you do first?
⚠ Common exam trap
The trap here is accepting a subjective performance complaint as an actionable engineering requirement and choosing a plausible-sounding optimization before establishing a measured baseline.
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
✓
Ask the director to define the target latency, the workflow step where it feels slow, and how many descriptions are generated per session, then measure current performance against that definition.
A vague performance complaint must be converted into measurable criteria before any engineering commitment. Asking for the target latency, the specific workflow step, and the typical session volume establishes both a baseline and an agreed success threshold. That measurement reveals whether the bottleneck is model generation, prompt size, batching, network overhead, or the surrounding application, so effort goes to the real constraint rather than a guess.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Escalate to the account executive to reset expectations, explaining that large language model latency is inherent and cannot be improved.
Why it's wrong here
Declaring latency immutable is technically false: prompt size, model selection, concurrency, caching, and streaming all materially affect response time. Escalating before investigating also signals that engineering will not engage with a legitimate business concern. This response forfeits the chance to identify a real, fixable bottleneck and damages the working relationship with the client.
- ✗
Agree to the deadline and immediately begin optimizing prompt length and switching to a smaller model to reduce latency.
Why it's wrong here
Acting on an unquantified complaint can degrade output quality: shortening prompts and downgrading the model may cut latency while producing descriptions the marketing team rejects. Without a measured baseline you cannot demonstrate improvement or verify the fix. This approach trades a communication problem for a quality problem and still may not hit the real bottleneck.
- ✓
Ask the director to define the target latency, the workflow step where it feels slow, and how many descriptions are generated per session, then measure current performance against that definition.
Why this is correct
Turning a subjective complaint into measurable criteria, such as p95 time to first usable description for a typical batch, lets you locate the actual bottleneck before choosing a remedy. It also creates an agreed success threshold so the director can confirm the fix worked. Measurement first prevents committing engineering effort to a change that may not address the real constraint.
- ✗
Convert the pipeline to streaming responses so text begins appearing in the UI sooner, which will resolve the perceived slowness.
Why it's wrong here
Streaming improves perceived responsiveness by showing tokens as they arrive, but it does not reduce total generation time and may not address the director's actual complaint. If the underlying issue is queueing, batch size, or retry storms, streaming masks it temporarily. Committing to this change before quantifying the problem risks spending effort on a symptom rather than the measured cause.
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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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