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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 2: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 3: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 4: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 5: Context Management & Reliability | 15% | - Context handling
|
>> Reliable CCAR-F Exam Topics <<
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NEW QUESTION # 100
When researching "renewable-energy adoption," the web-search agent returns recent statistics showing 35% adoption in 2024, while the document-analysis agent extracts an 18% adoption figure from an internal 2021 report. The synthesis agent incorrectly treats the figures as contradictory instead of recognizing that they may show growth over time. What change would best enable the synthesis agent to interpret such temporal differences correctly?
Answer: A
Explanation:
The two statistics cannot be compared correctly without temporal metadata. Option A preserves when each value was measured, not merely when its document was retrieved. The structured record should ideally distinguish publication date, observation period, geographic scope, population, methodology, unit, and metric definition. The synthesis agent can then determine whether the figures represent a trend, conflicting measurements of the same period, or incomparable populations.
Anthropic's multi-agent research guidance emphasizes specifying output formats and source requirements so downstream agents receive the context required for accurate synthesis. Its structured-output capability enables these provenance fields to be required and schema validated.
Option B removes valuable historical evidence and still does not guarantee that returned sources describe the same measurement period. Option C assumes newer automatically means more reliable and could discard an authoritative historical baseline. Option D preserves older material but imposes an interpretation without examining whether methodology, geography, or definitions changed. Explicit temporal and methodological metadata allows the synthesis agent to state a defensible conclusion such as apparent growth from 18% in
2021 to 35% in 2024 while preserving any comparability caveats.
NEW QUESTION # 101
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 processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: D
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
NEW QUESTION # 102
Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?
Answer: C
Explanation:
Including a request_review flag based on confidence thresholds, along with reasons for review, gives the agent clear guidance on when human intervention is needed. This reduces both unnecessary reviews and errors caused by low-confidence extractions, improving overall reliability and efficiency.
NEW QUESTION # 103
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 pipeline runs:
PROMPT= ' You are a code reviewer. Analyze the provided diff for bugs, security issues, and style violations.
'
claude -p \
--dangerously-skip-permissions \
--system-prompt " $PROMPT " \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text-it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules.
Which change to the invocation will cause Claude to inspect related repository files while still applying your custom review instructions?
Answer: C
Explanation:
Option D preserves Claude Code's standard coding-agent instructions while adding the specialized review criteria. Anthropic documents that --system-prompt replaces the entire default system prompt, including its tool guidance, safety instructions, and coding conventions. It does not technically disable tools, but removing that guidance can make the invocation behave like a narrowly scoped text processor. --append-system-prompt retains the default behavior and layers the review instructions on top.
The prompt should explicitly direct Claude to use Read, Glob, and Grep to inspect definitions, callers, tests, and related modules whenever the diff alone is insufficient. Option A is incorrect because --system-prompt is not incompatible with tools. Option B is also inaccurate: --allowedTools pre-approves tool execution; it does not make tools available when -p would otherwise disable them. In this command, --dangerously-skip- permissions already bypasses permission prompts. Option C is false because Claude Code officially supports piped standard input in non-interactive mode. The repaired invocation should therefore use --append-system- prompt and include an explicit repository-exploration requirement. Claude Code programmatic usage , CLI system-prompt reference
NEW QUESTION # 104
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file; unchanged files are not included. Reviews are posted asynchronously and do not block pull-request creation. Developers report that reviews consistently miss bugs involving cross-file interactions-for example, a pull request renames a function's parameters, but the review does not flag callers in other files that still use the old parameter names. Post-release analysis shows that cross-file bugs account for 35% of production incidents from reviewed pull requests. What is the most effective change to your review design?
Answer: D
Explanation:
The failure is caused by missing evidence, not insufficient reasoning over the supplied evidence. A static prompt containing only changed files cannot reliably identify callers, configuration dependencies, generated interfaces, or indirect relationships located elsewhere in the repository. Asking Claude to reason more deeply cannot recover code that it was never given.
Option A converts the review into a bounded agentic loop. Claude can search for symbol references, read relevant callers, inspect type definitions, and follow newly discovered dependencies before validating a potential defect. Anthropic describes the Claude Code agentic loop as gathering context, taking action, verifying results, and repeating based on tool feedback. A turn limit preserves predictable cost and execution time.
Option C improves coverage but imposes an arbitrary two-hop boundary and depends on the accuracy of a precomputed graph, which may omit dynamic imports, reflection, generated code, configuration references, or language-specific call relationships. Option D creates duplicated context and aggregation complexity while still limiting each reviewer to predetermined dependents. Option B changes the reasoning instructions but supplies no mechanism for checking unchanged files. Tool-enabled, just-in-time retrieval is therefore the most adaptable and reliable architecture for cross-file review.
NEW QUESTION # 105
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