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Cómo se puntúan las preguntas basadas en escenarios

Puntuación escalada de 100 a 1000, 720 para aprobar y sin penalización por adivinar.

Cómo se puntúan las preguntas basadas en escenarios es una lección gratuita de Claude Architect en CoddyKit. Esta es la lección 1 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Claude Architect, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Claude Architect incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

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 under

Each 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_reason

Pacing 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 on

Turning 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.

Preguntas frecuentes

¿La lección «Cómo se puntúan las preguntas basadas en escenarios» es gratis?

Sí — el texto completo de «Cómo se puntúan las preguntas basadas en escenarios» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Claude Architect, actualiza a CoddyKit PRO. El curso de Claude Architect incluye 4 lecciones en total.

¿Qué aprenderé en «Cómo se puntúan las preguntas basadas en escenarios»?

Puntuación escalada de 100 a 1000, 720 para aprobar y sin penalización por adivinar. Practicas Claude Architect con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar Claude Architect?

No se requiere experiencia previa. Claude Architect en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 1 de 4.

¿Cuánto tiempo toma la lección «Cómo se puntúan las preguntas basadas en escenarios»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de Claude Architect?

Sí. Cada lección de Claude Architect incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

  1. Cómo se puntúan las preguntas basadas en escenarios
  2. Lectura de un prompt basado en escenarios
  3. Eliminación de respuestas incorrectas
  4. Recorrido completo por un examen de prueba
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