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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Claude API and Client SDKs
|
| Topic 2: Model Selection and Optimization | 16.8% | - Model Selection
|
| Topic 3: Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Topic 4: Eval, Testing, and Debugging | 2.6% | - Testing and Debugging
|
| Topic 5: Security and Safety | 8.1% | - Safety and Guardrails
|
| Topic 6: Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Topic 7: Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Topic 8: Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
>> CCDV-F Exam Lab Questions <<
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NEW QUESTION # 19
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: A
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 20
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?
Answer: D
Explanation:
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .
NEW QUESTION # 21
Your Claude application validates structured output but has been treating validation failures as terminal errors. Each validation failure causes the entire user request to fail. The team wants to handle validation failures more gracefully.
How would you handle the validation failures?
Answer: A
Explanation:
C converts validation failure from an uncontrolled terminal condition into a first-class recoverable error path . The supplied exam item identifies C as correct. If structured output does not meet the application's contract, it should never be forwarded as though valid, but immediate user-visible failure is also unnecessary when bounded recovery is possible.
A robust flow can retry generation, ask Claude to repair the malformed structure using the validation error as feedback, switch to an approved fallback path, or ultimately return a controlled failure if the retry budget is exhausted. The application must cap these recovery attempts to avoid unbounded loops.
Anthropic's Structured Outputs documentation explains that unconstrained model generation can produce parsing errors, missing fields, inconsistent types, or schema violations that otherwise require error handling and retries. Current Structured Outputs can eliminate many schema-level failures through constrained decoding, although exceptional conditions such as refusal or output truncation still require explicit handling.
A violates the validation boundary. B removes a protective control. D transfers an engineering reliability responsibility to end users.
Relevant Claude Developer topics: Claude App Design, structured output, validation, retries, repair loops, fallback logic, bounded recovery, error paths, and resilient downstream integration .
NEW QUESTION # 22
You are setting up the configuration management approach for a new Claude Code project. Your team will use CLAUDE.md files and settings.json files to control behavior, and you want to make sure changes are tracked and reviewable.
The configuration management approach would...
Answer: A
Explanation:
D applies configuration as code and is the answer explicitly selected in the supplied examination source.
Claude Code treats project configuration as part of the repository's working context. Anthropic documents project-level CLAUDE.md as the mechanism for providing project instructions and context, while shared project configuration belongs in .claude/settings.json. Crucially, Anthropic states that .claude/settings.json is intended to be checked into source control and shared with the team .
Keeping these files alongside the code establishes a single versioned relationship between application behavior and the configuration that influences Claude Code. Standard pull requests then provide diffs, peer review, ownership controls, CI validation, audit history, and straightforward rollback. A particular commit can therefore reproduce both source code and its associated Claude project configuration.
A creates competing copies and synchronization risk. B artificially separates behavior configuration from the code version it affects. C creates the same problem specifically for settings.json.
Developer-local values remain distinct: .claude/settings.local.json exists specifically for settings that should not be committed.
Relevant Claude Developer topics: Confia Management, CLAUDE.md, settings.json, configuration as code, source control, pull-request review, reproducibility, auditability, and team-shared Claude Code configuration .
NEW QUESTION # 23
Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?
Answer: B
Explanation:
The supplied question marks C , and Anthropic's API documentation directly supports it. Claude API rate limits are enforced using dimensions such as requests per minute (RPM), input tokens per minute (ITPM), and output tokens per minute (OTPM) . Anthropic also notes that short traffic bursts can exceed effective limits even when a longer-term average appears acceptable.
Applications should therefore determine the organization's actual configured limits, model peak traffic against those boundaries, throttle or queue work as necessary, and handle 429 responses correctly. Rate-limit responses include a retry-after value indicating when another request should be attempted. Anthropic's official SDKs automatically retry transient connection failures, rate-limit errors, and server errors with exponential backoff by default.
Streaming does not exempt requests from rate limits, making A technically incorrect. Consolidating payloads in B may reduce request count but could increase token consumption and does not by itself address all rate- limit dimensions. D may smooth traffic, but it is only one optimization and does not substitute for rate-limit- aware application logic.
Relevant Claude Developer topics: Claude API Mechanics, rate limits, RPM, ITPM, OTPM, HTTP 429, retry-after, exponential backoff, throttling, and capacity planning .
NEW QUESTION # 24
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