场景题如何评分
分数范围为 100–1000，达到 720 分即可通过，不因猜错扣分。
场景题如何评分 是 CoddyKit 上的免费 Claude Architect 课时。 这是第 1 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Claude Architect 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Claude Architect 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
What "Scoring" Really Measures
Every question on the Claude Certified Architect exam is a scenario-based multiple-choice item: 4 options, exactly 1 correct. Scoring is the process that turns your pattern of right and wrong answers into a single number the certification body can pass or fail you on.
This lesson is about that machinery: how raw answers become a scaled score, what the 720 threshold means, and why the no-guessing-penalty rule changes how you should behave on every single item.
Raw Score vs. Scaled Score
Your raw score is simply how many items you answered correctly. The exam does not report that number to you. Instead it converts your raw performance onto a fixed scaled range of 100 to 1000.
Scaling exists so that different exam forms — which may draw different scenarios of slightly different difficulty — remain comparable. A 720 on an easier form and a 720 on a harder form represent the same demonstrated competence. You never compute the scale yourself; you just need to clear the line.
# Conceptual model: raw correctness -> fixed scaled band
SCALE_MIN, SCALE_MAX = 100, 1000
PASS = 720
# Two forms, different raw difficulty, same scaled bar
for form, scaled in [("easy form", 725), ("hard form", 718)]:
print(form, "->", "PASS" if scaled >= PASS else "FAIL")The 720 Pass Line
The pass mark is 720 on the 100-1000 scale — not 72%. Because the score is scaled rather than a raw percentage, you cannot reliably translate "720" into "answered N of M correctly."
The practical consequence: aim for a comfortable margin above 720. Treating 720 as your target leaves you one or two unlucky items away from failing. Architects who pass consistently are operating well clear of the line, not flirting with it.
PASS = 720
def result(scaled_score: int) -> str:
margin = scaled_score - PASS
status = "PASS" if margin >= 0 else "FAIL"
return f"{status} (margin {margin:+d})"
print(result(745)) # comfortable
print(result(721)) # razor-thin
print(result(715)) # just underEach Item Is Scored Independently
Items are scored independently: each question contributes on its own. There is no partial credit within a 4-option item — you either selected the single correct option or you didn't.
Crucially, 4 of the 8 scenarios are shown on any given sitting, and every question on those four scenarios is scored. None of them is a throwaway. There is no "only the hardest count" rule and no dropped-lowest-item mechanic to rely on.
No Penalty for Guessing
This is the single most important scoring rule for your behavior: a wrong answer and a blank both score zero. Wrong answers are not subtracted from your raw score.
That makes leaving an item blank strictly inferior to answering it. A blank guarantees zero points; any answer — even an uninformed one — has a non-zero chance of being correct. The rule is simple and absolute: answer every question.
# Why a blank is never optimal under no-penalty scoring
blank_ev = 0.0 # blank: guaranteed zero
blind_guess_ev = 1 / 4 # 4 options, 1 correct
print("Blank EV:", blank_ev)
print("Blind guess EV:", blind_guess_ev)
print("Always answer:", blind_guess_ev > blank_ev)Elimination Multiplies Your Odds
Because there's no penalty, every option you can confidently rule out raises the expected value of guessing. A blind guess on four options is worth 1/4; eliminate one and it's 1/3; eliminate two and you're at 1/2.
On this exam the easiest options to eliminate are the anti-patterns — parsing model text for words like "done" to end a loop, using iteration caps as the primary stop mechanism, enforcing critical business rules with prompts instead of hooks, or requiring schema fields that may be absent. Spot one and you've usually found a wrong answer.
# Expected value rises as you eliminate distractors
def guess_ev(remaining_options: int) -> float:
return 1 / remaining_options
for remaining in (4, 3, 2):
print(f"{remaining} options left -> EV {guess_ev(remaining):.2f}")Scoring Reaches Across All 5 Domains
The scored items span the five weighted domains, and the weights describe how the exam is composed:
- D1 Agent Architecture & Orchestration — 27%
- D2 Tool Design & MCP — 18%
- D3 Claude Code Config & Workflows — 20%
- D4 Prompt Engineering & Structured Output — 20%
- D5 Context Management & Reliability — 15%
D1 carries the heaviest weight, so points there move your scaled score the most. But a weak domain you skip is points permanently lost — there's no domain you can safely ignore.
DOMAIN_WEIGHTS = {
"D1 Agent Architecture & Orchestration": 27,
"D2 Tool Design & MCP": 18,
"D3 Claude Code Config & Workflows": 20,
"D4 Prompt Engineering & Structured Output": 20,
"D5 Context Management & Reliability": 15,
}
assert sum(DOMAIN_WEIGHTS.values()) == 100
print("Heaviest:", max(DOMAIN_WEIGHTS, key=DOMAIN_WEIGHTS.get))Only the Single Best Option Scores
Many scenario items have more than one technically valid-looking option. Scoring credits only the single best answer for the stated context — the one that reflects sound Claude architecture for that exact situation.
That's why surface plausibility isn't enough. "It could work" loses to "it's the correct production practice here." The exam is testing the applied judgment of an architect with 6+ months of production Claude experience, not your ability to recognize a defensible-sounding option.
How a Scenario Question Tests You
A scored item drops you into a real situation and asks what to do next. The correct option maps to a fact-sheet principle; the distractors map to anti-patterns. Example shape: an agentic loop returns stop_reason == "tool_use" — what's the right control flow?
The credited answer terminates on stop_reason, runs tools, appends results to the full history, and repeats until end_turn. Distractors would parse the assistant's text for "done" or hard-stop on an iteration cap. Recognizing the principle behind the scenario is what earns the point.
# The credited control flow on a scored loop item
while True:
resp = client.messages.create(
model=MODEL, max_tokens=1024, messages=history, tools=tools
)
if resp.stop_reason == "tool_use":
history.append({"role": "assistant", "content": resp.content})
history.append(run_tools(resp)) # append results, continue
continue
break # end_turn / max_tokens / stop_sequence -> stop on stop_reasonPacing Protects Your Score
Since blanks score zero and every item is independent, a slow pace is a scoring risk: time spent agonizing over one item is time stolen from items you could answer correctly. Don't let a single hard scenario starve the rest of the exam.
A disciplined loop: read the scenario, eliminate the anti-patterns, commit to the strongest remaining option, and flag-and-move if you're stuck. You can revisit flagged items, but you must never run out of time with answers left blank — that's points you forfeited for free.
# A simple per-item triage you can run in your head
def triage(seconds_spent, eliminated):
if eliminated >= 3:
return "answer now (1 option left)"
if seconds_spent > 90:
return "commit best guess, flag, move on"
return "keep eliminating anti-patterns"
print(triage(120, 1)) # commit best guess, flag, move onTurning the Scoring Rules Into a Plan
The scoring model rewards a specific strategy:
- Answer 100% of items — no penalty means a blank is pure forfeited expected value.
- Eliminate anti-patterns first to push each guess from 1/4 toward 1/2.
- Pick the single best option for the context, not merely a workable one.
- Spread prep across all 5 domains, weighting D1 (27%) most, since you can't choose which 4 of 8 scenarios appear.
- Build margin above 720 — treat 720 as the floor, not the goal.
Quick Check: Scoring Mechanics
Apply what you've learned about how scenario questions are scored.
Recap: How Scoring Works
Key takeaways on how scenario questions are scored:
- Scaled, not raw: performance is mapped onto a fixed 100-1000 scale so forms stay comparable.
- Pass = 720 on that scale (not 72%) — aim for margin above it.
- Independent items: 4 of 8 scenarios shown, every question scored, no partial credit, single best option only.
- No guessing penalty: blanks and wrong answers both score zero, so answer everything.
- Eliminate anti-patterns to raise each guess from 1/4 toward 1/2.
- Cover all 5 domains (D1 27%, D2 18%, D3 20%, D4 20%, D5 15%) and pace yourself so nothing is left blank.
常见问题解答
「场景题如何评分」课时是免费的吗？
是的 — 「场景题如何评分」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Claude Architect 课程的其余内容，请升级到 CoddyKit PRO。 Claude Architect 课程共包含 4 节课。
「场景题如何评分」这节课中我会学到什么？
分数范围为 100–1000，达到 720 分即可通过，不因猜错扣分。 你通过在浏览器中直接运行的动手代码来练习 Claude Architect，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Claude Architect 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Claude Architect 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 1 节课，共 4 节。
「场景题如何评分」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Claude Architect 课中编写并运行代码吗？
能。每节 Claude Architect 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。