[Q48-Q65] Anthropic CCA-F Dumps Updated [Oct-2026] Get 100% Real Exam Questions!

Q48. When implementing multi-turn conversations with tool usage in the Claude API, how must the application handle the follow-up request after executing a tool?

Pass the tool result inside an assistant message with an is_error flag.

Send only the new tool result to save token context space in the context window.

Append a user message containing a tool_result block with the matching tool_use_id, and include the full prior conversation history.

Use the /compact command to summarize the tool output before returning it.

Q49. When using Claude Code for continuous integration (CI) and multi-pass code review workflows, which of the following practices are highly recommended? Choose 2 correct answers.

Use the -p flag to ensure non-interactive execution so the pipeline does not hang.

Have the same session that generated the code review its own changes to ensure maximum context preservation.

Isolate the code generator session from the code reviewer session to eliminate confirmation bias.

Use the Message Batches API to process blocking pre-merge pull request checks.

Q50. Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns “industry analysts estimate $50B market size (methodology varies)” and the document analysis agent returns “peer-reviewed study estimates $35B (±$7B, 95% CI),” the coordinator either picks one arbitrarily or produces vague statements like “the market may be
$35B-$50B depending on factors.” What systematic approach best addresses this?

Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.

Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.

Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.

Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.

Q51. A data extraction pipeline uses few-shot examples to teach Claude how to extract invoice line items. To ensure the model handles unexpected formats reliably, what must be included in the few-shot examples?

At least one example that demonstrates how to handle an ambiguous scenario or edge case.

Only examples of perfectly formatted, standard invoices to establish a strong baseline.

A negative example showing the model generating conversational text instead of JSON.

Extensive XML tags defining the model’s underlying neural architecture.

Q52. When implementing a multi-agent research system for a enterprise application, a coordinator agent must delegate a market analysis task to a specialized subagent. What is the correct approach for passing context to this subagent?

Share the entire coordinator conversation history to ensure the subagent has maximum context.

Allow the subagent to access a shared global state object where the coordinator writes its findings.

Pass only the specific, explicit context relevant to the subagent’s assigned subtask in the prompt.

Use the /compact command to summarize the coordinator history before passing it to the subagent.

Q53. To extract metadata from different types of reports, a system uses an enum field for document _ type. New, unexpected report types are frequently added over time. What is the recommended schema design pattern to handle this ambiguity?

Include an other enum value paired with a string field for extensible categorization.

Use an open-ended string field instead of an enum to maximize flexibility.

Define a rigid enum with only currently known types and rely on the model to find the closest match.

Implement a PostToolUse hook to automatically drop unknown document types.

Q54. To extract metadata from a highly variable set of invoices, you use an enum field for document_type. How should you handle unexpected document types to maintain extraction quality?

Include an other enum value paired with a detail string field for extensible categorization.

Omit the enum constraint entirely and use an open-ended string field.

Implement a PreToolUse hook to drop unrecognized document types.

Provide an enum containing only known types and rely on the model to find the closest match.

Q55. 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, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match – the file has repetitive docstrings, variable names, and structural patterns.
What’s the most reliable way to complete this insertion?

Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness

Use Bash to append the function definition to the end of the file using heredoc syntax

Use Edit’s replace_all parameter to target a common pattern and embed the new function in the replacement text

Use Read to load the file, add the function at the appropriate location, then Write the updated file

Q56. A quicktechie.com automated code review system is generating a high rate of false positives for a specific category, causing developers to ignore the tool’s feedback. What is the recommended strategy to restore developer trust?

Increase the sampling size of the review batch.

Switch to the Message Batches API for asynchronous processing.

Temporarily disable the high false-positive categories while improving the prompts for those specific categories.

Require a 100 percent human-in-the-loop review for all findings.

Q57. 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 documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What’s the most efficient first diagnostic step?

Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.

Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.

Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.

Q58. Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to “remove old backups,” but company policy requires archiving backup files. Both tools currently have minimal descriptions: “Archives a file” and
“Deletes a file.” Which change most directly improves tool selection for this scenario?

Expand tool descriptions to clarify use cases, adding guidance like “Do not use for backup files” to delete_file.

Add a confirmation step that requires users to type “CONFIRM DELETE” before delete_file executes.

Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.

Add few-shot examples to the system prompt demonstrating that requests involving “backup” or
“old” should use archive_file.

Q59. Which of the following statements correctly describe best practices for integrating Claude Code into a CI/CD pipeline? Choose 2 correct answers.

Using the Message Batches API for blocking pre-merge checks to maximize cost savings.

Using the -p flag for non-interactive execution so the pipeline does not hang waiting for user input.

Separating the code generation session from the code review session to ensure the reviewer evaluates the code objectively.

Running Claude Code in interactive mode to allow developers to answer clarifying questions during the build process.

Q60. When designing JSON schemas for an extraction tool, which of the following practices are highly recommended for reliability? Choose 2 correct answers.

Setting tool_choice: “auto” to guarantee structural compliance across all documents.

Defining fields as optional or nullable (e.g., “[“string “null”]) to prevent fabrication of missing data.

Including an other enum value alongside a detail string field to handle ambiguous or extensible categories.

Omitting the required array entirely to give the model maximum flexibility.

Q61. When orchestrating an agentic workflow using the Claude Agent SDK, you must enforce a strict business rule: the agent is never allowed to issue refunds over $500. What is the most robust and deterministic method to implement this constraint?

Add a capitalized warning in the system prompt: NEVER process a refund exceeding $500.

Implement a PostToolUse hook that intercepts the process_refund tool inputs, checks the amount, and programmatically blocks execution if it exceeds 500.

Configure the agent’s tool_choice parameter to auto so it can decide when a refund is inappropriate.

Provide few-shot examples showing the agent refusing to process a $600 refund.

Q62. 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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer submits two requests:
– Request A: “Rename the getUserData function to fetchUserProfile
everywhere it’s used.”
– Request B: “Improve error handling throughout the data processing
module – add try/catch blocks, meaningful error messages, and ensure
failures don’t silently corrupt data.”
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

Neither request benefits significantly

Request A, the function rename task

Both requests benefit equally

Request B, the error handling task

Q63. An engineering team notices that Claude occasionally provides answers beyond the company’s internal policy documents. They want responses to rely only on approved documentation whenever possible. Which solution is MOST appropriate?

Increase temperature to 1.0

Use Retrieval-Augmented Generation (RAG)

Shorten every prompt

Disable system prompts

Q64. During an agentic loop execution at quicktechie.com, Claude requests to use a lookup_customer tool, but the backend database is down, resulting in an access failure. What is the architecturally correct way to return the result for the next iteration?

Option A

Option B

Option C

Option D

Q65. After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about “code dependencies.” Examining tool definitions reveals:
MCP: analyze_ dependencies – “Analyzes dependency graph”
Built-in: Grep – “Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context.”
What’s the most effective approach to improve the agent’s selection of MCP tools?

Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.

Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.

Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.

Expand MCP tool descriptions to detail capabilities and outputs – e.g., “Builds dependency graph showing direct imports, transitive dependencies, and cycles.”

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