Real CCAR-F Dumps – Anthropic Correct Answers updated on 2026 [Q46-Q64]

QUESTION 46
You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools-Read, Write, Bash, Grep, and Glob-and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.

Use Grep to search for the regular-expression pattern eval( across all files in the codebase.

Read the project’s main entry file and follow import statements to trace where eval() might be used.

Use Bash to run ls -R | grep eval and search the recursively listed filenames.

QUESTION 47
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
What is the most appropriate approach?

Use direct execution to make the change.

Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.

Enter plan mode first to create a detailed implementation strategy before making the change.

Enter plan mode to analyze how the validation might impact other parts of the reservation flow.

QUESTION 48
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?

Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.

Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.

Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.

Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.

QUESTION 49
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?

Pre-fill Claude’s response with an opening brace to force JSON output, then complete and parse the response.

Append instructions like “Output only valid JSON matching the schema exactly” and implement retry logic to re-prompt when JSON parsing fails.

Define a tool with your target schema as input parameters and have Claude call it with the extracted data.

Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude’ s text response as JSON.

QUESTION 50
During testing, when a customer says, “I need a refund for my recent purchase,” the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.

Change tool_choice from auto to any so Claude must call a tool on every turn.

Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.

Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.

QUESTION 51
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?

Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.

Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.

Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.

Escalate immediately to a human agent since the refund action cannot be completed.

QUESTION 52
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.

Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.

Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude’ s output to schema-compliant JSON.

Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.

QUESTION 53
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

Implement post-processing that uses keyword matching to filter out findings containing terms such as
“convention,” “context-dependent,” or “trade-off.”

Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.

Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.

Add instructions to your system prompt to “be conservative,” “only flag definite issues,” and “consider that some patterns may be intentional.”

QUESTION 54
In addition to your CI pipeline, your organization has enabled Claude’s managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?

Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.

Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.

Add custom review instructions to a GitHub Actions workflow file, using the action’s prompt parameter to suppress duplicate lint findings, ignore generated template code, and impose stricter evidence requirements on rendering-related issues.

Add detailed explanations to the project’s CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.

QUESTION 55
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking-developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?

Whether reducing per-review latency from 30-60 seconds to near-instant matters for your workflow.

Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.

Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.

Whether you can structure each review as a single request without multi-turn refinement.

QUESTION 56
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What’s the most effective basis for selecting which extractions to route for human review?

Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.

Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

Route extractions for review only when downstream systems report data quality issues or processing failures.

Randomly sample 5% of extractions for review.

QUESTION 57
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like “a handful” or “a splash,” the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

Fine-tune the model on the 847 corrected extractions.

Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.

Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.

Update your JSON schema to add a “measurement_type” enum field (precise/informal).

QUESTION 58
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You’ve asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You’ve noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What’s the most effective approach for iterating toward a working solution?

Start fresh with a detailed prompt specifying all formatting requirements upfront.

Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.

Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks-testing after each change.

Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.

QUESTION 59
After deploying the automated review, you notice high precision but low recall-real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to “only report high- confidence issues you are certain about” and “err on the side of not commenting.” Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

Add detailed few-shot examples demonstrating bug categories Claude should flag-race conditions, null dereferences, and error-handling gaps-while retaining the high-confidence filtering instruction.

Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.

Split the review into a finding stage whose objective is comprehensive coverage-reporting every potential issue with confidence and severity metadata-and a separate stage that verifies and thresholds those findings.

Expand the context to include related tests, recent Git history, and the module’s dependency graph so Claude has richer evidence for judging severity.

QUESTION 60
The synthesis agent receives summarized findings from the web-search and document-analysis agents, then passes a consolidated summary to the report generator. During testing, you discover that the generated reports make factual claims without proper citations-the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What is the most effective approach to ensure proper source attribution in the final reports?

Have the report generator query the web-search agent to relocate sources for claims in the final report.

Have each agent output structured data that separates content summaries from source metadata, including URLs, document names, and page numbers.

Skip summarization and pass the complete raw outputs from the web-search and document-analysis agents directly to the report generator.

Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.

QUESTION 61
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.

Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.

Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.

QUESTION 62
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

The order details are added to the conversation and the model reasons about which action to take.

The orchestration layer automatically routes to the next tool based on the order’s status field.

The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.

The agent executes the remaining steps in a tool sequence planned at the start of the request.

QUESTION 63
A user expands the research system beyond its original web-search agent by adding specialized data sources.
A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news- monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality?

Standardize all subagent outputs as prose summaries with inline citations.

Standardize all subagent outputs as JSON containing fields for claim, evidence, source, and confidence.

Update the synthesis agent to render each content type appropriately-financial data as tables, news as prose, and technology areas as structured lists.

Add a format-conversion layer that transforms every subagent result into a common intermediate representation before synthesis.

QUESTION 64
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: “Only flag critical issues that would definitely cause production failures.
Ignore minor concerns and anything you are uncertain about.” Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable.
Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

Add a second review pass that rereads the diff using the same prompt, looking for anything the first pass may have missed.

Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

Remove all severity-related instructions from the prompt and let the model use its default judgment about what to report.

Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.

Real CCAR-F Dumps – Anthropic Correct Answers updated on 2026 [Q46-Q64]

Be the first to reply

Leave a Reply Cancel reply