---
title: "What Learners Get Right and Wrong | Cred Farmer"
url: https://credfarmer.com/stats
description: "Domain-by-domain accuracy from timed practice papers on our own question bank, with sample sizes, confidence intervals, and what we refuse to claim."
last_updated: 2026-10-03
publisher: "Cred Farmer, an independent study platform, not affiliated with or endorsed by Anthropic or Pearson VUE"
---

Practice data

# What learners get right and wrong

This is how people score on Cred Farmer’s own practice questions under a timed clock. It is not the real exam, it is not a pass rate, and where the numbers cannot tell two things apart we say so.

- 1,561 · Timed practice papers sat

  Drills and study runs are not among them
- 829 · Learners who sat one

  292 of 829 left their first paper unfinished
- 31,899 · Answers in the measured set

  16,084 unanswered and 2,273 repeat encounters removed
- 31.3% · Papers left less than half answered

  489 of 1,561 papers
- Read on 3 October 2026. The third figure counts only answers that survive every filter below; the first two count everything, filtered or not. Each bar is drawn against the count printed above it, and none of them is an accuracy.

Read this first

## What this page is not

- **It is not the real exam.** Every figure here comes from our practice bank: questions we wrote, marked against our own answer key. The certification exams are written and delivered by other people, and nothing measured here transfers to them.
- **It is not a pass rate.** We have verified outcomes for almost nobody, and we already published the reason we would not quote one even if we did, on our [scoring methodology page](https://credfarmer.com/compare/methodology). Nothing here names a score threshold, and nothing here should be divided by one.
- **It is not a claim about candidates.** Everyone in this data chose a preparation site and then chose to sit a timed paper on it. That is a self-selected group and it is not a sample of anything wider.
- **It is not an ordering.** The tables are sorted so they can be read, not to rank anything. Almost every gap between two domains here is smaller than the uncertainty around it, and we say which ones are not.

CCDV-F

## Claude Certified Developer – Foundations, domain by domain

223 learners, 386 timed papers and 16,028 answers that survive every filter below.

Accuracy by exam domain

Pooled across every domain, **68.4%** of answers were correct, 67.69% to 69.14% counting items, and 66.0% to 70.84% once the answers are grouped by the learner who gave them. Where two bands overlap, this data cannot tell those domains apart.

Every pairwise contrast

Every pairwise contrast. 28 contrasts between the 8 published rows. 16 of them separates after correction; the other 12 sit inside the noise, which means the measurement cannot tell those rows apart.

- Claude Code with Security and Safety: this contrast separates after correction.
- Tools and MCPs with Security and Safety: this contrast separates after correction.
- Prompt and Context Engineering with Security and Safety: this contrast separates after correction.
- Eval, Testing, and Debugging with Security and Safety: this contrast separates after correction.
- Applications and Integration with Security and Safety: this contrast separates after correction.
- Applications and Integration with Claude Code: this contrast separates after correction.
- Applications and Integration with Tools and MCPs: this contrast separates after correction.
- Applications and Integration with Prompt and Context Engineering: this contrast separates after correction.
- Agents and Workflows with Security and Safety: this contrast separates after correction.
- Agents and Workflows with Claude Code: this contrast separates after correction.
- Agents and Workflows with Tools and MCPs: this contrast separates after correction.
- Agents and Workflows with Prompt and Context Engineering: this contrast separates after correction.
- Model Selection and Optimization with Security and Safety: this contrast separates after correction.
- Model Selection and Optimization with Claude Code: this contrast separates after correction.
- Model Selection and Optimization with Tools and MCPs: this contrast separates after correction.
- Model Selection and Optimization with Prompt and Context Engineering: this contrast separates after correction.

One cell per pair. Filled survives correction for testing them all; open is inside the noise.

Of the 28 possible comparisons between these domains, 16 survive once the intervals are grouped by learner and corrected for testing them all: learners answer Applications and Integration about 18 points more accurately than Security and Safety; learners answer Model Selection and Optimization about 20 points more accurately than Security and Safety; learners answer Agents and Workflows about 19 points more accurately than Security and Safety; learners answer Agents and Workflows about 10 points more accurately than Tools and MCPs; learners answer Model Selection and Optimization about 10 points more accurately than Tools and MCPs; learners answer Agents and Workflows about 9 points more accurately than Prompt and Context Engineering; learners answer Applications and Integration about 8 points more accurately than Tools and MCPs; learners answer Model Selection and Optimization about 9 points more accurately than Prompt and Context Engineering; learners answer Prompt and Context Engineering about 11 points more accurately than Security and Safety; learners answer Tools and MCPs about 10 points more accurately than Security and Safety; learners answer Applications and Integration about 7 points more accurately than Prompt and Context Engineering; learners answer Eval, Testing, and Debugging about 15 points more accurately than Security and Safety; learners answer Model Selection and Optimization about 10 points more accurately than Claude Code; learners answer Agents and Workflows about 10 points more accurately than Claude Code; learners answer Applications and Integration about 8 points more accurately than Claude Code; learners answer Claude Code about 9 points more accurately than Security and Safety. Every other gap in this table is inside the noise.

The two rows furthest apart, and why both intervals are printed

**The two rows furthest apart, and why both intervals are printed**

| Row | Accuracy | Per-item interval | Learner-clustered interval | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| Security and Safety | 53.3% | 50.45% to 56.15% | 49.14% to 57.49% | 628 of 1,178 | 196 |
| Model Selection and Optimization | 73.0% | 71.39% to 74.64% | 70.51% to 75.59% | 2,106 of 2,883 | 223 |

Both intervals agree that the gap between these two is real.

**Claude Certified Developer – Foundations accuracy by exam domain, on timed practice papers taken on the current question bank.**

| Domain | Answered correctly | 95% interval per item | 95% interval clustered by learner | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| 7 · Security and Safety | 53.3% | 50.45% to 56.15% | 49.14% to 57.49% | 628 of 1,178 | 196 |
| 3 · Claude Code | 62.8% | 58.79% to 66.65% | 58.29% to 67.32% | 363 of 578 | 185 |
| 8 · Tools and MCPs | 62.9% | 60.54% to 65.14% | 59.3% to 66.44% | 1,065 of 1,694 | 199 |
| 6 · Prompt and Context Engineering | 63.9% | 61.63% to 66.17% | 60.22% to 67.64% | 1,099 of 1,719 | 209 |
| 4 · Eval, Testing, and Debugging | 68.3% | 63.63% to 72.61% | 62.87% to 73.72% | 280 of 410 | 187 |
| 2 · Applications and Integration | 71.3% | 70.04% to 72.49% | 68.97% to 73.59% | 3,743 of 5,251 | 215 |
| 1 · Agents and Workflows | 72.7% | 70.8% to 74.44% | 69.88% to 75.43% | 1,682 of 2,315 | 200 |
| 5 · Model Selection and Optimization | 73.0% | 71.39% to 74.64% | 70.51% to 75.59% | 2,106 of 2,883 | 223 |

**CCDV-F detail by objective: 24 shown, 1 withheld · An objective appears here once at least 20 learners have answered at least 100 of its questions. The rest are not shown, rather than shown small.**

**Claude Certified Developer – Foundations accuracy by exam objective, on timed practice papers taken on the current question bank.**

| Objective | Answered correctly | 95% interval per item | Correct of answered | Learners |
| --- | --- | --- | --- | --- |
| O01 · Agent Architecture | 78.7% | 75.4% to 81.57% | 534 of 679 | 188 |
| O02 · Agent Construction with Claude | 64.6% | 61.15% to 67.89% | 498 of 771 | 198 |
| O03 · Agent Patterns and Frameworks | 75.1% | 72.15% to 77.91% | 650 of 865 | 200 |
| O04 · Understanding Requirements | 45.8% | 41.62% to 49.98% | 249 of 544 | 189 |
| O05 · Systems Life Cycle | 57.1% | 52.21% to 61.87% | 229 of 401 | 160 |
| O06 · Claude API Mechanics | 82.5% | 80.19% to 84.65% | 926 of 1,122 | 214 |
| O07 · Software Engineering Foundations | 81.5% | 79.22% to 83.55% | 1,012 of 1,242 | 214 |
| O08 · Claude Application Design | 70.5% | 67.92% to 72.99% | 878 of 1,245 | 205 |
| O09 · Configuration Management | 64.4% | 60.79% to 67.89% | 449 of 697 | 194 |
| O10 · Claude Code Operation | 62.8% | 58.79% to 66.65% | 363 of 578 | 185 |
| O11 · Debugging and Error Handling | 68.3% | 63.63% to 72.61% | 280 of 410 | 187 |
| O12 · LLM Fundamentals | 79.3% | 76.61% to 81.79% | 748 of 943 | 221 |
| O13 · Technical Fundamentals | 75.9% | 73.29% to 78.36% | 829 of 1,092 | 213 |
| O14 · Model Selection and Tradeoffs | 53.3% | 48.84% to 57.67% | 260 of 488 | 206 |
| O15 · Cost and Token Management | 74.7% | 69.98% to 78.94% | 269 of 360 | 202 |
| O16 · Context Engineering | 70.2% | 66.44% to 73.77% | 420 of 598 | 202 |
| O17 · Prompt Engineering | 62.5% | 58.68% to 66.14% | 403 of 645 | 207 |
| O18 · Output Handling | 58.0% | 53.5% to 62.34% | 276 of 476 | 200 |
| O19 · AI Application Security | 56.9% | 52.52% to 61.1% | 290 of 510 | 184 |
| O20 · Guardrails and Safe Deployment | 41.8% | 36.28% to 47.43% | 124 of 297 | 185 |
| O21 · Claude Hooks | 52.9% | 47.34% to 58.32% | 166 of 314 | 159 |
| O23 · Tool Implementation | 60.3% | 56.53% to 64.02% | 394 of 653 | 196 |
| O24 · MCP Server Development | 64.8% | 60.07% to 69.27% | 267 of 412 | 193 |
| O25 · Agentic Customization | 64.2% | 60.4% to 67.88% | 404 of 629 | 198 |

Intervals here are per item only. These cells are far smaller than the domain cells above, and this page makes no claim that any objective differs from any other.

CCAO-F

## Claude Certified Associate – Foundations, domain by domain

197 learners, 297 timed papers and 10,384 answers that survive every filter below.

Accuracy by exam domain

Pooled across every domain, **60.4%** of answers were correct, 59.44% to 61.33% counting items, and 57.67% to 63.11% once the answers are grouped by the learner who gave them. Where two bands overlap, this data cannot tell those domains apart.

Every pairwise contrast

Every pairwise contrast. 21 contrasts between the 7 published rows. 9 of them separates after correction; the other 12 sit inside the noise, which means the measurement cannot tell those rows apart.

- Product and Model Selection with Prompting and Task Execution: this contrast separates after correction.
- Governance, Risk, and Responsible Use with Prompting and Task Execution: this contrast separates after correction.
- Troubleshooting and Optimization with Prompting and Task Execution: this contrast separates after correction.
- Troubleshooting and Optimization with Output Evaluation and Validation: this contrast separates after correction.
- Workflow Integration and Solution Design with Prompting and Task Execution: this contrast separates after correction.
- Workflow Integration and Solution Design with Output Evaluation and Validation: this contrast separates after correction.
- Workflow Integration and Solution Design with Configuration and Knowledge Management: this contrast separates after correction.
- Workflow Integration and Solution Design with Product and Model Selection: this contrast separates after correction.
- Workflow Integration and Solution Design with Governance, Risk, and Responsible Use: this contrast separates after correction.

One cell per pair. Filled survives correction for testing them all; open is inside the noise.

Of the 21 possible comparisons between these domains, 9 survive once the intervals are grouped by learner and corrected for testing them all: learners answer Workflow Integration and Solution Design about 14 points more accurately than Prompting and Task Execution; learners answer Workflow Integration and Solution Design about 11 points more accurately than Output Evaluation and Validation; learners answer Workflow Integration and Solution Design about 9 points more accurately than Configuration and Knowledge Management; learners answer Workflow Integration and Solution Design about 8 points more accurately than Product and Model Selection; learners answer Troubleshooting and Optimization about 9 points more accurately than Prompting and Task Execution; learners answer Governance, Risk, and Responsible Use about 8 points more accurately than Prompting and Task Execution; learners answer Workflow Integration and Solution Design about 7 points more accurately than Governance, Risk, and Responsible Use; learners answer Product and Model Selection about 6 points more accurately than Prompting and Task Execution; learners answer Troubleshooting and Optimization about 6 points more accurately than Output Evaluation and Validation. Every other gap in this table is inside the noise.

The two rows furthest apart, and why both intervals are printed

**The two rows furthest apart, and why both intervals are printed**

| Row | Accuracy | Per-item interval | Learner-clustered interval | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| Prompting and Task Execution | 54.4% | 52.05% to 56.74% | 51.04% to 57.77% | 945 of 1,737 | 196 |
| Workflow Integration and Solution Design | 68.7% | 66.36% to 70.99% | 65.05% to 72.38% | 1,061 of 1,544 | 127 |

Both intervals agree that the gap between these two is real.

**Claude Certified Associate – Foundations accuracy by exam domain, on timed practice papers taken on the current question bank.**

| Domain | Answered correctly | 95% interval per item | 95% interval clustered by learner | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| 1 · Prompting and Task Execution | 54.4% | 52.05% to 56.74% | 51.04% to 57.77% | 945 of 1,737 | 196 |
| 2 · Output Evaluation and Validation | 57.5% | 55.44% to 59.43% | 54.18% to 60.72% | 1,361 of 2,369 | 161 |
| 5 · Configuration and Knowledge Management | 59.4% | 56.35% to 62.29% | 54.94% to 63.77% | 625 of 1,053 | 125 |
| 3 · Product and Model Selection | 60.5% | 57.77% to 63.23% | 57.41% to 63.66% | 747 of 1,234 | 130 |
| 6 · Governance, Risk, and Responsible Use | 62.0% | 59.49% to 64.52% | 58.4% to 65.68% | 889 of 1,433 | 119 |
| 7 · Troubleshooting and Optimization | 63.4% | 60.4% to 66.33% | 59.6% to 67.23% | 643 of 1,014 | 117 |
| 4 · Workflow Integration and Solution Design | 68.7% | 66.36% to 70.99% | 65.05% to 72.38% | 1,061 of 1,544 | 127 |

**CCAO-F detail by objective: 28 shown, 0 withheld · An objective appears here once at least 20 learners have answered at least 100 of its questions. The rest are not shown, rather than shown small.**

**Claude Certified Associate – Foundations accuracy by exam objective, on timed practice papers taken on the current question bank.**

| Objective | Answered correctly | 95% interval per item | Correct of answered | Learners |
| --- | --- | --- | --- | --- |
| O01 · Create effective prompts for business and technical tasks | 45.9% | 41.2% to 50.64% | 195 of 425 | 189 |
| O02 · Apply task decomposition techniques to structure complex requests | 60.0% | 55.62% to 64.23% | 297 of 495 | 176 |
| O03 · Iterate prompts to improve output quality | 52.3% | 47.35% to 57.18% | 206 of 394 | 177 |
| O04 · Adapt prompting strategies based on task type (e.g., analysis, research, drafting, brainstorming) | 58.4% | 53.64% to 63.0% | 247 of 423 | 157 |
| O05 · Evaluate Claude-generated outputs for accuracy and completeness | 50.6% | 45.66% to 55.63% | 194 of 383 | 158 |
| O06 · Identify hallucinations, inconsistencies, and biases in responses | 64.0% | 59.0% to 68.62% | 243 of 380 | 153 |
| O07 · Apply fact-checking and validation techniques | 61.2% | 56.13% to 66.01% | 227 of 371 | 149 |
| O08 · Determine when human review or additional verification is required | 66.0% | 61.1% to 70.53% | 254 of 385 | 143 |
| O09 · Edit, adapt, refine, and compare the output generated by Claude model(s) for the intended audience | 60.5% | 54.72% to 65.99% | 173 of 286 | 137 |
| O10 · Organize and curate information for optimal outputs and select appropriate output formats (artifacts for code/documents, inline for conversational responses, structured formats for data) | 47.9% | 43.77% to 52.0% | 270 of 564 | 133 |
| O11 · Select appropriate Claude product features (e.g., projects, research mode, chat, artifacts) | 53.5% | 47.39% to 59.44% | 139 of 260 | 122 |
| O12 · Differentiate between Claude model types (e.g., Haiku, Sonnet, Opus) | 46.0% | 40.13% to 51.9% | 125 of 272 | 126 |
| O13 · Align model selection with task requirements (e.g., cost, speed, quality) | 71.2% | 66.29% to 75.57% | 259 of 364 | 126 |
| O14 · Understand, manage context limitations and memory considerations and when to restart, summarize, or persist | 66.3% | 61.07% to 71.11% | 224 of 338 | 127 |
| O15 · Apply Claude to analyze requirements and use cases | 69.0% | 63.8% to 73.76% | 227 of 329 | 124 |
| O16 · Leverage Claude for research, planning, and process optimization | 75.8% | 70.21% to 80.58% | 197 of 260 | 121 |
| O17 · Integrate Claude into existing workflows to augment or redesign workflows | 63.5% | 60.06% to 66.78% | 499 of 786 | 123 |
| O18 · Communicate Claude’s value and limitations to stakeholders | 81.7% | 75.14% to 86.77% | 138 of 169 | 119 |
| O19 · Configure Claude Projects with instructions and knowledge sources | 55.7% | 50.69% to 60.54% | 216 of 388 | 123 |
| O20 · Manage uploaded knowledge and connectors (e.g., Google Drive, Gmail) | 61.9% | 56.99% to 66.52% | 245 of 396 | 120 |
| O21 · Create effective system-level instructions | 61.0% | 55.02% to 66.61% | 164 of 269 | 115 |
| O22 · Identify appropriate and inappropriate use cases | 71.0% | 65.68% to 75.74% | 220 of 310 | 116 |
| O23 · Apply data, privacy sensitivity, regulatory policies, and privacy considerations | 63.0% | 58.95% to 66.78% | 367 of 583 | 115 |
| O24 · Follow organizational AI policies and governance standards | 64.7% | 59.19% to 69.85% | 198 of 306 | 114 |
| O25 · Understand ethical implications of AI usage | 44.4% | 38.21% to 50.85% | 104 of 234 | 108 |
| O26 · Identify, diagnose, and resolve issues with underperforming prompts and/or poor outputs | 57.9% | 52.32% to 63.24% | 180 of 311 | 105 |
| O27 · Adjust approach based on feedback and results | 66.4% | 62.04% to 70.48% | 318 of 479 | 115 |
| O28 · Optimize workflows for efficiency and effectiveness | 64.7% | 58.27% to 70.7% | 145 of 224 | 109 |

Intervals here are per item only. These cells are far smaller than the domain cells above, and this page makes no claim that any objective differs from any other.

CCAR-F

## Claude Certified Architect – Foundations, domain by domain

75 learners, 130 timed papers and 5,487 answers that survive every filter below.

Accuracy by exam domain

Pooled across every domain, **78.3%** of answers were correct, 77.22% to 79.41% counting items, and 74.84% to 81.82% once the answers are grouped by the learner who gave them. Where two bands overlap, this data cannot tell those domains apart.

Every pairwise contrast

Every pairwise contrast. 10 contrasts between the 5 published rows. 6 of them separates after correction; the other 4 sit inside the noise, which means the measurement cannot tell those rows apart.

- Agentic Architecture & Orchestration with Prompt Engineering & Structured Output: this contrast separates after correction.
- Agentic Architecture & Orchestration with Tool Design & MCP Integration: this contrast separates after correction.
- Agentic Architecture & Orchestration with Claude Code Configuration & Workflows: this contrast separates after correction.
- Context Management & Reliability with Prompt Engineering & Structured Output: this contrast separates after correction.
- Context Management & Reliability with Tool Design & MCP Integration: this contrast separates after correction.
- Context Management & Reliability with Claude Code Configuration & Workflows: this contrast separates after correction.

One cell per pair. Filled survives correction for testing them all; open is inside the noise.

Of the 10 possible comparisons between these domains, 6 survive once the intervals are grouped by learner and corrected for testing them all: learners answer Agentic Architecture & Orchestration about 15 points more accurately than Tool Design & MCP Integration; learners answer Context Management & Reliability about 17 points more accurately than Tool Design & MCP Integration; learners answer Context Management & Reliability about 17 points more accurately than Prompt Engineering & Structured Output; learners answer Agentic Architecture & Orchestration about 15 points more accurately than Prompt Engineering & Structured Output; learners answer Context Management & Reliability about 14 points more accurately than Claude Code Configuration & Workflows; learners answer Agentic Architecture & Orchestration about 11 points more accurately than Claude Code Configuration & Workflows. Every other gap in this table is inside the noise.

The two rows furthest apart, and why both intervals are printed

**The two rows furthest apart, and why both intervals are printed**

| Row | Accuracy | Per-item interval | Learner-clustered interval | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| Prompt Engineering & Structured Output | 70.6% | 67.71% to 73.26% | 65.22% to 75.9% | 731 of 1,036 | 56 |
| Context Management & Reliability | 87.9% | 85.58% to 89.96% | 84.77% to 91.1% | 751 of 854 | 69 |

Both intervals agree that the gap between these two is real.

**Claude Certified Architect – Foundations accuracy by exam domain, on timed practice papers taken on the current question bank.**

| Domain | Answered correctly | 95% interval per item | 95% interval clustered by learner | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| 4 · Prompt Engineering & Structured Output | 70.6% | 67.71% to 73.26% | 65.22% to 75.9% | 731 of 1,036 | 56 |
| 2 · Tool Design & MCP Integration | 71.2% | 68.25% to 73.9% | 66.58% to 75.72% | 703 of 988 | 57 |
| 3 · Claude Code Configuration & Workflows | 74.4% | 71.78% to 76.92% | 69.82% to 79.05% | 824 of 1,107 | 74 |
| 1 · Agentic Architecture & Orchestration | 85.8% | 83.96% to 87.5% | 82.56% to 89.08% | 1,289 of 1,502 | 67 |
| 5 · Context Management & Reliability | 87.9% | 85.58% to 89.96% | 84.77% to 91.1% | 751 of 854 | 69 |

**CCAR-F detail by objective: 29 shown, 1 withheld · An objective appears here once at least 20 learners have answered at least 100 of its questions. The rest are not shown, rather than shown small.**

**Claude Certified Architect – Foundations accuracy by exam objective, on timed practice papers taken on the current question bank.**

| Objective | Answered correctly | 95% interval per item | Correct of answered | Learners |
| --- | --- | --- | --- | --- |
| O01 · Design and implement agentic loops for autonomous task execution | 75.0% | 69.94% to 79.46% | 237 of 316 | 66 |
| O02 · Orchestrate multi-agent systems with coordinator-subagent patterns | 85.0% | 79.21% to 89.44% | 159 of 187 | 54 |
| O03 · Configure subagent invocation, context passing, and spawning | 92.5% | 88.14% to 95.33% | 197 of 213 | 63 |
| O04 · Implement multi-step workflows with enforcement and handoff patterns | 93.5% | 89.41% to 96.11% | 202 of 216 | 56 |
| O05 · Apply Agent SDK hooks for tool call interception and data normalization | 88.8% | 83.37% to 92.65% | 159 of 179 | 59 |
| O06 · Design task decomposition strategies for complex workflows | 79.1% | 73.33% to 83.92% | 178 of 225 | 54 |
| O07 · Manage session state, resumption, and forking | 94.6% | 90.01% to 97.13% | 157 of 166 | 52 |
| O08 · Design effective tool interfaces with clear descriptions and boundaries | 65.5% | 58.45% to 71.83% | 125 of 191 | 54 |
| O09 · Implement structured error responses for MCP tools | 73.7% | 67.19% to 79.38% | 146 of 198 | 54 |
| O10 · Distribute tools appropriately across agents and configure tool choice | 67.4% | 60.36% to 73.7% | 126 of 187 | 54 |
| O11 · Integrate MCP servers into Claude Code and agent workflows | 66.0% | 59.05% to 72.28% | 128 of 194 | 53 |
| O12 · Select and apply built-in tools (Read, Write, Edit, Bash, Grep, Glob) effectively | 81.7% | 75.98% to 86.23% | 178 of 218 | 54 |
| O13 · Configure CLAUDE.md files with appropriate hierarchy, scoping, and modular organization | 70.7% | 65.83% to 75.17% | 256 of 362 | 70 |
| O14 · Create and configure custom slash commands and skills | 64.4% | 57.54% to 70.64% | 130 of 202 | 67 |
| O15 · Apply path-specific rules for conditional convention loading | 92.2% | 85.41% to 96.02% | 95 of 103 | 67 |
| O16 · Determine when to use plan mode vs direct execution | 84.1% | 76.22% to 89.68% | 95 of 113 | 69 |
| O18 · Integrate Claude Code into CI/CD pipelines | 73.7% | 67.76% to 78.94% | 174 of 236 | 52 |
| O19 · Design prompts with explicit criteria to improve precision and reduce false positives | 62.5% | 56.31% to 68.4% | 152 of 243 | 56 |
| O20 · Apply few-shot prompting to improve output consistency and quality | 74.8% | 66.87% to 81.39% | 101 of 135 | 55 |
| O21 · Enforce structured output using tool use and JSON schemas | 56.3% | 48.5% to 63.71% | 90 of 160 | 53 |
| O22 · Implement validation, retry, and feedback loops for extraction quality | 74.0% | 66.57% to 80.31% | 114 of 154 | 54 |
| O23 · Design efficient batch processing strategies | 76.2% | 69.58% to 81.78% | 141 of 185 | 54 |
| O24 · Design multi-instance and multi-pass review architectures | 83.7% | 77.11% to 88.59% | 133 of 159 | 51 |
| O25 · Manage conversation context to preserve critical information across long interactions | 90.4% | 84.91% to 93.98% | 150 of 166 | 63 |
| O26 · Design effective escalation and ambiguity resolution patterns | 86.4% | 79.77% to 91.14% | 121 of 140 | 57 |
| O27 · Implement error propagation strategies across multi-agent systems | 96.0% | 90.9% to 98.27% | 119 of 124 | 62 |
| O28 · Manage context effectively in large codebase exploration | 90.5% | 84.2% to 94.52% | 115 of 127 | 57 |
| O29 · Design human review workflows and confidence calibration | 76.5% | 69.02% to 82.71% | 111 of 145 | 56 |
| O30 · Preserve information provenance and handle uncertainty in multi-source synthesis | 88.8% | 82.81% to 92.9% | 135 of 152 | 59 |

Intervals here are per item only. These cells are far smaller than the domain cells above, and this page makes no claim that any objective differs from any other.

Not yet published

## The other certifications

Same computation, same code path, not enough learners yet. A table of its own is earned at 30 distinct learners, and not before.

Learners per certification, against the floor

**Learners per certification, against the floor**

| Certification | Learners | Floor | Tables published |
| --- | --- | --- | --- |
| CCDV-F: Claude Certified Developer – Foundations | 223 | 30 | yes |
| CCAO-F: Claude Certified Associate – Foundations | 197 | 30 | yes |
| CCAR-F: Claude Certified Architect – Foundations | 75 | 30 | yes |
| CCAR-P: Claude Certified Architect – Professional | 23 | 30 | no |

Short of the line we publish no accuracy table and no objective detail: not a partial one, not a summary. The count is all this page says.

- CCAR-P · Claude Certified Architect – Professional

  **23** learners have sat a timed paper on the current question bank, across 40 papers. We publish accuracy by domain at 30 learners.

A certification with fewer than 5 learners is left off this page altogether, including its learner count. At that size a domain profile is close enough to one identifiable person’s results that publishing the shape of it, even as a card saying how few there are, is not something we are willing to do.

Hundreds of learners, not dozens

## How people behave under the clock

Far more data than the accuracy tables, because these depend on what a learner did rather than on which question they were served.

### Most papers that go wrong are never finished

31.3% · 489 of 1,561 timed papers, 29.07% to 33.68%

A paper counts as abandoned when fewer than half the questions answered. An unanswered question is marked wrong when a paper is scored, so this is not a low score; it is a paper that was left.

Every timed paper, and the median abandoned one

Every timed paper, and the median abandoned one. 489 of 1,561 timed papers were left less than half answered: 31.3%, 29.07% to 33.68%. 80 of those were left entirely blank. 292 of 829 learners did so on a first paper: 35.2%, 32.04% to 38.54%. The median abandoned paper had 4 of 60 questions answered, handed in at 16.6 minutes.

No outcome and no advice attached: this is a description of what happens, and the largest single pattern in everything we hold.

### The flag button knows something

−32.1pp · within-learner difference, −36.0pp to −28.12pp, 308 learners

Comparing each learner against themselves (items they flagged against items they did not), flagged items come back markedly less accurate. The difference spans every practice answer, not timed papers alone: the button is used far more often away from the clock.

The within-learner difference

The within-learner difference. flagged minus unflagged: −32.1pp, −36.0pp to −28.12pp, over 308 learners. Measured on 566 of 4,133 sittings, sittings where the flag button was used at all.

The two levels, on the current bank

**The two levels, on the current bank**

| Row | Accuracy | Per-item interval | Learner-clustered interval | Correct of answered |
| --- | --- | --- | --- | --- |
| Flagged for review | 48.9% | not published | not published | 940 of 1,922 |
| Not flagged | 75.4% | not published | not published | 11,050 of 14,656 |

Absolute levels are published on the current bank only, where they mean something. No interval is published for either, so none is drawn.

Only 566 of 4,133 practice sittings carry a flag at all, so this describes people who use the button. Read it as evidence that the button works (that learners can tell which of their own answers are shaky) and not as evidence that flagging changes anything.

### People are less right than they feel when they are certain

Accuracy by what the learner said about the answer

- Sure of the answer and Fairly confident: these two separate once answers are clustered inside learners.
- Fairly confident and Guessing: these two separate once answers are clustered inside learners.

A bracket says whether that step survives clustering the answers inside learners. A dashed one does not.

**Accuracy by the learner's own confidence rating, recorded against each question before the paper was marked.**

| What the learner said | Answered correctly | 95% interval per item | 95% interval clustered by learner | Correct of answered | Learners |
| --- | --- | --- | --- | --- | --- |
| Sure of the answer | 85.9% | 84.5% to 87.28% | 82.66% to 89.23% | 2,079 of 2,419 | 179 |
| Fairly confident | 63.6% | 61.73% to 65.46% | 60.72% to 66.51% | 1,633 of 2,567 | 246 |
| Guessing | 48.3% | 45.58% to 50.96% | 44.55% to 51.98% | 640 of 1,326 | 234 |

answers tagged “Sure of the answer” come back more accurate than answers tagged “Fairly confident”; answers tagged “Fairly confident” come back more accurate than answers tagged “Guessing”.

Every self-rated answer on the current question bank, in any mode: a wider population than the tables above, and a small self-selected one: only learners who rate their own answers are in it. We publish it because our readiness model assumes far more of a “sure” than this measures.

Methodology

## What we counted, and what we threw out

Seven filters, each applied to whatever the one before it left. Nothing on this page was typed in by a person.

Seven filters, in the order they are applied

The first three leave 938 timed papers on the current question bank

1. Our own accounts4 papers

   **Why**

   We test the product on the product. Those runs are not learners, so they go first.
2. Papers sat on the retired question bank668 papers

   **Why**

   The bank in use until the August release was far too easy to measure anything with. It is more than half of everything we hold, and it appears on this page only in the section that explains why it was dropped.
3. Drills and untimed study papers2,116 papers

   **Why**

   Drills deliberately re-serve questions the learner previously got wrong, so drill accuracy measures the queue rather than the learner. Study mode has no clock, which makes it an open-book instrument. Three instruments reported as one number is not a measurement.
4. Papers whose question bank moved underneath them219 papers

   **Why**

   For every remaining paper we rebuild, from today’s bank, exactly which domains and objectives it presented, and require an exact match with what was recorded at the time. Where it does not match, the question at a given position is no longer the question that was served, and the paper is dropped rather than guessed at.
5. Questions that were never answered16,084 answers

   **Why**

   An unanswered question is marked wrong when a paper is scored, which is right for a score and wrong for a measurement. Every figure above counts only questions a learner actually completed.
6. Second and later encounters with the same question2,273 answers

   **Why**

   Only a learner’s first answer to each question counts. Repeat sittings score several points above first sittings, which is memory rather than learning, and pooling them would flatter every figure on this page.
7. Questions already seen answered in a drill1,331 answers

   **Why**

   Drills use the same questions as the timed papers, and they show the answer: when a learner checks one, and for every question on the review afterwards. A question the learner had already seen answered in a drill before submitting the paper tests memory rather than a first encounter, so that answer is dropped, exactly as the readiness score drops it.

The last four leave 31,899 answers

Bars are scaled within their unit (papers against papers, answers against answers), never across the two. A step that removed nothing shows no bar.

**Why there are two intervals**

Answers are not independent of one another. They arrive in papers, and papers belong to learners, so one learner having a bad afternoon moves many answers at once. The per-item interval a reader expects, and which every published figure here carries, ignores that, and is measurably too narrow as a result. Grouping the answers by learner gives an interval several times wider, and that wider one is the honest measure of how much this sample can settle. Both are printed at the same weight, in the same row, so nobody has to take our word for which is which.

Interval bounds are printed at the precision they were computed to, and rounded outward rather than to the nearest tenth. Rounding a bound inward would publish a narrower claim than the data supports.

**A learner is an account, not a person**

Every count on this page counts accounts. Signing in with two providers makes two of them, and we do not link accounts across providers, so every learner figure here is an upper bound on how many distinct people are behind it. That is one of two reasons the threshold for publishing a table sits as high as 30.

**The measured answers cover a short window**

The banks were edited in place without a version change, so an answer recorded before that edit can no longer be matched to the question that was actually served. The fourth filter above drops those, and it drops a great many of them, which means the accuracy tables describe a narrow and recent slice of history rather than everything we hold. That is a real limit, and we would rather print it than leave it to be inferred.

**Answers on the earlier question banks were given on longer questions, and some carry an answer-key cue**

Until the question bank labelled 2026-10-02 went live, the questions were longer than the official sample questions. Outside CCAR-F the typical question ran up to about twice their length, and as many as two in five questions on a paper asked for more than one answer. That bank rewrote every paper in the official form: shorter questions and options, four options and one answer on all but a few questions, and “select two” on at most one question in ten.

Until the bank labelled 2026-09-30 went live, the correct option on a single-answer question also tended to be longer than the wrong ones, the longest option on a multiple-answer question was almost always a wrong one, and on CCAR-F the correct option was usually option B. That bank rewrote the options so the key no longer stands out by length, and spread the keys evenly across the letters.

Where this page says the current question bank, it means every bank it counts, and those include the earlier ones. So the accuracy in its tables includes answers given on the longer questions and, on the earliest banks, the cue as well, and a gap between two domains measured on them partly reflects how the items were written rather than only what learners knew.

The retired question bank

## Why we threw away more than half the data

The only section where figures from the retired bank appear at all, and the clearest evidence we have that a question bank, and not a learner, produces most of what looks like a finding.

Take one domain, “Prompting and Task Execution”, and put all 7 domains of that certification in order of accuracy. On the retired bank it sat in sixth place counting up from the lowest. On the current bank it sits in first place, 38 points lower than it was.

On the retired bank

92.3% · 90.3% to 93.86% · 811 of 879

On the current bank

54.4% · 52.05% to 56.74% · 945 of 1,737

Same platform, same domain, same kind of learner, opposite conclusion. Nothing about the people changed between those two figures. The questions did, and that is the entire argument for excluding the 2026-07-23 bank, and the 404 timed papers sat on it, from every accuracy figure on this page.

**What these two figures do not license**

Neither of those two orderings is one we would publish on its own. The gaps inside each of them are smaller than the uncertainty around them, and the certification this domain belongs to may not have enough learners to earn a table anywhere on this page. The movement between the two banks is the part that is not in doubt: the two intervals above do not come close to meeting.

A second symptom of the same problem. On the retired bank, of 209 papers with no unanswered questions, 209 cleared our own scoring threshold, the lowest of them scoring 745 on a thousand-point scale. An instrument that separates nobody is not measuring anything.

The same release also renumbered domains for CCAR-F, so papers sat before it carry domain numbers that no longer exist. There is no archive of the pre-release tree, which is why those papers cannot simply be remapped.

An earlier internal draft of this page read the first of those figures as a fact about people and drew the opposite conclusion to the one above. It was a property of the questions. This section is here so that mistake stays on the record instead of in the archive.

The negative results

## What we measured and would not publish

Four things we computed, looked at, and left out, because a reader cannot otherwise tell a claim we cannot support from one we never thought to make.

Whether studying first improves your first paper

The differences do not survive on the current bank, and the striking version comes from the retired one.

**Why**

On the current bank the group with no study sections behind them and the group with several are not distinguishable, and the ordering is not even consistent. The striking version of this (a large gap in first-paper scores) comes almost entirely from the retired bank and from unfinished papers, and quoting a score against a threshold would be a back door to exactly the claim we refuse above.

A list of the concepts people get wrong

Structurally impossible rather than merely underpowered.

**Why**

Each question carries a concept label, and those labels are unique per question: one distinct value for every item in the bank. Any list built from them is a list of individual questions wearing a costume, so we do not build one.

How many learners pass

Off the table permanently, not withheld out of modesty.

**Why**

This is not a pass rate we are withholding out of modesty. It is off the table permanently, for the reason already published on our [methodology page](https://credfarmer.com/compare/methodology). Note the trap: the retired bank would have supported a spectacular-sounding version of it. That sentence would have been true, checkable, and a fact about a broken question bank.

Anything at all about the certification exams themselves

Our questions, our answer key, our self-selected learners.

**Why**

Every sentence on this page is scoped to our practice bank, and where you see a domain name it describes how our items for that domain were answered, and nothing further.

Figures read on 3 October 2026 from question bank versions 2026-10-02 and 2026-09-30 and 2026-09-18 and 2026-08-20 (excluding 2026-07-23). Every number here is recomputed from the answer records rather than restated, by the same code that renders the page you are reading. How we decide what may be published at all is on our [methodology page](https://credfarmer.com/compare/methodology). Not affiliated with, or endorsed by, Anthropic or Pearson VUE.