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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Model Selection and Optimization	16.8%	- Model Capabilities and Trade-offs - Cost and Latency Optimization - Performance Optimization - Model Selection
Topic 2: Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging
Topic 3: Applications and Integration	33.1%	- Claude API and Client SDKs - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development - Multimodal and Structured Outputs
Topic 4: Tools and MCPs	10.6%	- Building Custom Tools and MCP Servers - Tool Use and Tool Schemas - Model Context Protocol
Topic 5: Security and Safety	8.1%	- Secure Tool Use and Guardrails - Safety and Responsible Development - Application Security - Prompt Injection and Untrusted Content
Topic 6: Claude Code	3.1%	- Claude Code Configuration and Extensibility
Topic 7: Agents and Workflows	14.7%	- Agent Architecture - Agent Construction with Claude - Agent Patterns and Frameworks
Topic 8: Prompt and Context Engineering	11%	- Context Engineering - Context Management and Long-Context Techniques - Prompt Engineering

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
B. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
D. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.

Answer: B

NEW QUESTION # 24
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
B. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
C. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
D. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.

Answer: D

Explanation:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.

NEW QUESTION # 25
Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

A. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.
B. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
C. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.
D. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.

Answer: C

Explanation:
Option D follows the fundamental optimization rule of measuring before changing. Without feature-level usage telemetry, the team cannot know whether cost growth comes from request volume, long prompts, excessive outputs, low cache-hit rates, expensive models, agent loops, tool results, or one particularly inefficient workflow.
Anthropic's Usage and Cost API exists specifically to provide granular historical usage and cost information.
It supports token tracking and breakdowns by dimensions including model, workspace, service tier, API key, context window, and other usage characteristics. Anthropic describes this data as useful for monitoring, cost reconciliation, optimization, and determining whether system changes improve efficiency.
B applies a model downgrade indiscriminately and may damage features whose quality requirements genuinely demand a stronger model. C similarly imposes an arbitrary token reduction without identifying where waste exists. A abandons cost management entirely.
The correct lifecycle is instrument, establish a baseline, identify high-cost features, analyze why they are expensive, apply targeted optimizations, and measure again. Relevant Study Guide topics: observability, usage telemetry, cost attribution, token accounting, production monitoring, capacity planning, and evidence- driven optimization.

NEW QUESTION # 26
Your team uses Claude Code across multiple repositories. You want the team's rules and general coding standards to apply to all repositories, and other rules to apply only to specific repositories. The team is currently duplicating instructions across every repository's CLAUDE.md file.
How would you address this?

A. Use a CLAUDE.md hierarchy that scopes general standards broadly and project-specific context within each repository's local CLAUDE.md.
B. Move all instructions to a separate documentation site that developers consult during Claude Code sessions across all repositories.
C. Stop using CLAUDE.md altogether and ask each developer to configure Claude Code manually for each project they work on.
D. Use a single repository's CLAUDE.md as the central source of truth and link to it from every other repository's CLAUDE.md file.

Answer: A

Explanation:
B is directly supported by both the supplied examination source and Claude Code's configuration model. The source marks the hierarchical CLAUDE.md approach as correct. Claude Code supports instructions at multiple scopes, allowing broadly applicable standards to be separated from project-specific context rather than duplicated across every repository.
Anthropic documents several CLAUDE.md scopes. Organization-managed instructions can apply broadly; user-level instructions in ~/.claude/CLAUDE.md apply across a user's projects; project instructions in .
/CLAUDE.md or ./.claude/CLAUDE.md provide repository-specific architecture, conventions, commands, and workflows. Claude Code loads applicable files according to the directory hierarchy, allowing broad instructions and more specific local instructions to coexist.
This arrangement improves maintainability because common coding standards are defined once at the appropriate scope, while each repository retains only the context unique to that project. A documentation website does not automatically inject rules into Claude Code context. C creates inconsistent manual configuration. D improperly couples unrelated repositories to one repository's configuration.
Relevant Claude Developer topics: Confia Management, CLAUDE.md hierarchy, organization scope, user scope, project scope, repository configuration, instruction inheritance, and configuration reuse .

NEW QUESTION # 27
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?

A. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.
B. Document the prompt version changes in the application changelog and proceed with the rollout, treating the staging test results as sufficient evidence of impact across all downstream systems.
C. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
D. Notify downstream system owners that a change is coming and schedule the rollout for the following week, without conducting a formal impact assessment.

Answer: A

Explanation:
A is the appropriate configuration-change control. The supplied examination item selects A . A prompt is not merely editorial text; in a Claude application it functions as executable behavioral configuration. Changing a prompt can alter output structure, field population, language, classifications, tool-use decisions, refusal behavior, or other assumptions on which downstream consumers depend.
Staging success therefore proves only the scenarios actually covered by staging. Before production rollout, the team must perform impact analysis across every dependent system, identify contractual expectations, run representative regressions, and coordinate changes where a consumer may be affected. This is especially important where downstream applications parse structured output or expect stable semantics.
Anthropic's Structured Outputs guidance illustrates why interface contracts matter: missing fields, inconsistent types, and schema violations can break consuming applications. Even when output remains syntactically valid, prompt changes can produce semantic changes that require consumer validation.
B assumes staging coverage is universal. C communicates the change without determining its consequences.
D arbitrarily defers systems instead of assessing them.
Relevant Claude Developer topics: Confia Management, configuration impact assessment, prompt versioning, dependency management, change control, downstream contracts, regression testing, and coordinated deployment .

NEW QUESTION # 28
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