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Pearson CCAR-F New Attempt

Page: 6 / 11
Total 152 questions

Claude Certified Architect – Foundations Questions and Answers

Question 21

The document-analysis agent has a single analyze_document tool that accepts a document and a free-text instruction parameter. During evaluation, requests such as “extract the key financial metrics” often return narrative summaries, while “summarize the methodology” sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require new requests with clarified instructions. What is the most effective way to improve reliability?

Options:

A.

Split the generic tool into purpose-specific tools—extract_data_points, summarize_content, and verify_claim_against_source—each with defined input and output contracts.

B.

Retain the single tool but add an analysis_type enum requiring explicit selection among extraction, summarization, and verification modes.

C.

Have the coordinator preclassify each analysis request before passing instructions to the document-analysis agent.

D.

Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats.

Question 22

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.

A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed.

What best explains this discrepancy?

Options:

A.

Claude retains the implementation context and prior decisions in the session, making it less likely to challenge assumptions underlying its own changes.

B.

The session’s context window necessarily became full, leaving insufficient capacity for meaningful review.

C.

The CI review must have used a more specific prompt, while the developer’s review request was too general.

D.

The CI environment can access the full repository, while a local Claude Code session can access only the current file.

Question 23

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 has spent 25 minutes exploring a game engine’s rendering subsystem—reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference “typical rendering patterns” rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.

What’s the most effective approach?

Options:

A.

Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.

B.

Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project’s CLAUDE.md.

C.

Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.

D.

Continue in the current context with more targeted prompts referencing the specific classes by name.

Question 24

The automated review consistently flags patterns your team uses intentionally—force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project’s conventions as persistent context during every review?

Options:

A.

Build post-processing keyword filters that suppress findings containing terms such as “force unwrap,” “large class,” or “deprecated import” before results reach developers.

B.

Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.

C.

Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.

D.

Document the team’s accepted patterns and intentional conventions in the project’s CLAUDE.md file so the model receives this context during every review.

Page: 6 / 11
Total 152 questions