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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering & Structured Output	20%	- Instruction design 1. Deterministic output formatting (e.g., JSON) 2. Few-shot prompting strategies - Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops
Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Coordinator / sub-agent patterns 2. Parallel task decomposition - Agentic loops & lifecycle design 1. Stop reason handling and control flow 2. Tool use loop execution (request → tool_use → result → next step)
Context Management & Reliability	15%	- System reliability 1. Escalation strategies (fail vs retry vs human) 2. Error propagation handling - Context window optimization 1. "Lost in the middle" mitigation strategies 2. Managing long conversation degradation
Claude Code Configuration & Workflows	20%	- CI/CD and workflow integration 1. Command vs plan mode usage 2. Automated pipeline integration - Claude Code configuration 1. CLAUDE.md hierarchy and scoping 2. Project-level vs user-level configuration
Tool Design & MCP Integration	18%	- Tool interface design 1. Structured tool input/output schemas 2. Clear tool descriptions and boundaries - Model Context Protocol (MCP) 1. MCP server integration patterns 2. Tool selection and routing strategies

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2026 High Pass-Rate CCA-F Exam Study Guide | Claude Certified Architect Foundations (CCA-F) 100% Free Updated Dumps

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q76-Q81):

NEW QUESTION # 76
Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

A. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
B. Keep all tools separate but implement response caching for analyze_article calls.
C. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.
D. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.

Answer: A

Explanation:
Combining discovery and analysis into a single tool call returns all candidate articles with their scores at once, allowing the agent to make informed editorial decisions efficiently. Keeping the collection addition separate preserves selective control while reducing sequential calls and latency.

NEW QUESTION # 77
Why is configuring the exact same Claude session to review its newly generated pull request code considered an architectural anti- pattern?

A. It exceeds the maximum token limit for the context window during the generation phase.
B. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
C. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.
D. The Message Batches API does not support multi-turn tool calling within a single request.

Answer: C

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively evaluating code.

NEW QUESTION # 78
When designing JSON schemas and interfaces for an agent's tools, which of the following practices represent recommended tool design principles? Choose 2 correct answers.

A. Keeping tool descriptions under 10 words to minimize context window token consumption.
B. Expanding tool descriptions to explicitly document what happens with empty inputs or invalid boundary data.
C. Splitting a generic document analysis tool into distinct, purpose-built extract_data and verify _ claim tools.
D. Combining all related backend API endpoints into a single monolithic tool to simplify the allowedTools array.

Answer: B,C

Explanation:
Recommended tool design principles include providing highly detailed descriptions (including edge cases and empty input handling) and splitting generic or monolithic tools into specialized, purpose-specific tools with clear input/output contracts. Minimal descriptions and monolithic tools severely degrade the LLM's ability to select and configure tools reliably.

NEW QUESTION # 79
When integrating Claude Code into continuous integration and delivery pipelines, which of the following practices are highly recommended for reliability and automation? Choose 2 correct answers.

A. Executing Claude Code using the -p flag to ensure the process runs non-interactively.
B. Isolating the code generator session from the code reviewer session to eliminate confirmation bias.
C. Having the same session that generated the code perform the final review to ensure context consistency.
D. Using the Message Batches API to execute blocking pre-merge pull request checks to save costs.

Answer: A,B

Explanation:
In CI/CD pipelines, using the -p (or --print) flag is essential to prevent interactive hangs. Furthermore, isolating generator and reviewer sessions guarantees an unbiased review. Same-session reviews create confirmation bias, and the Batch API is too slow for blocking pre- merge checks.

NEW QUESTION # 80
Which of the following represent architecturally sound strategies for managing context effectively across long documents or multi-agent handoffs? Choose 2 correct answers.

A. Placing key findings summaries at the beginning of aggregated inputs to mitigate position effects.
B. Replacing specific numeric constraints in long contexts with generalized natural language summaries.
C. Sharing the full coordinator conversation history with every subagent to guarantee maximum context.
D. Requiring subagents to output structured claim-source mappings to support accurate downstream synthesis.

Answer: A,D

Explanation:
Placing key findings at the beginning of inputs mitigates the lost in the middle effect, and requiring subagents to output structured claim-source mappings ensures accurate downstream synthesis. Sharing full coordinator history and using generalized summaries for numbers are heavily tested anti-patterns.

NEW QUESTION # 81
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