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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Eval, Testing, and Debugging | 2.6% | - Testing and Debugging
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
| Tools and MCPs | 10.6% | - Model Context Protocol
|
| Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
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NEW QUESTION # 69
Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?
Answer: C
Explanation:
Option D treats prompts as production configuration and executable application behavior rather than informal text. A prompt modification can change output format, tool-selection behavior, policy enforcement, downstream compatibility, token usage, or model quality just as materially as an ordinary source-code change. Consequently, prompt changes need traceability, review, testing, and rollback.
A single source of truth prevents different application paths from silently using inconsistent prompt versions.
Version control provides an immutable history showing who changed the prompt, what changed, and which application release used it. Pull-request review enables peer assessment and automated evaluation before deployment. The same configuration-as-code principle appears throughout Claude Code's project configuration model, where shared project configuration is designed to live with version-controlled project artifacts.
A removes governance and creates an uncontrolled external state. B increases hidden runtime behavior and bypasses normal review. C solves fragmentation but leaves the exact governance failure described in the scenario-the ability to ship prompt changes without review.
Therefore, D provides reproducibility and controlled change management. Relevant Study Guide topics:
prompt versioning, configuration management, source control, change review, reproducibility, regression testing, and rollback.
NEW QUESTION # 70
You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...
Answer: A
Explanation:
Production deployment does not terminate the software lifecycle. Once a Claude application is actively serving users, it enters an operations and maintenance phase in which developers and operators monitor performance, evaluate model behavior, respond to failures, control costs, manage security, and evolve the implementation as requirements or model capabilities change.
Anthropic's official developer documentation explicitly separates the journey into build, evaluate-and-ship, and operate stages. The operating stage includes workspace administration, API-key management, usage monitoring, and model migration. This confirms that production deployment is a transition into ongoing operation rather than the endpoint of development.
For LLM applications, maintenance is especially important because production traffic can expose input distributions and failure modes that were not fully represented during pre-release evaluation. Operational data should feed back into evaluations, prompt improvements, guardrails, architecture decisions, and model- version planning.
A incorrectly treats deployment as final. B may be useful but is only one governance activity, not the overall lifecycle phase. D assumes a mandatory organizational separation that is neither required nor generally desirable.
The supplied exam source marks C. Relevant topics: Systems Life Cycle, production operations, monitoring, maintenance, incident response, evaluation, and continuous system evolution.
NEW QUESTION # 71
You are designing a Claude application that will require structured JSON output for downstream processing.
The output schema is well-defined, and downstream systems will reject malformed JSON.
Answer: C
Explanation:
Option C establishes the strongest application boundary between probabilistic model generation and deterministic downstream processing. When another component requires JSON with a known contract, the application should explicitly define the expected structure and ensure that model output conforms to it before downstream execution. Anthropic's current Structured Outputs guidance states that structured outputs constrain responses to a specific schema and are intended to provide valid, parseable data for downstream processing. The current Claude API supports JSON Schema through output_config.format, while SDK helpers can additionally parse and validate returned data.
The underlying engineering principle remains the same even when structured-output enforcement is unavailable: never allow unvalidated model-generated structures to become trusted machine input. Option A provides insufficient contractual control. Option B defines the schema but pushes validation too late, increasing the probability that malformed or semantically invalid data reaches dependent components. Option D sacrifices machine reliability entirely.
Therefore, C correctly combines schema specification, explicit format guidance, and validation. This corresponds to Claude Developer topics covering structured outputs, defensive application design, schema validation, and reliable model-to-system interfaces. The question and options are reproduced from the supplied examination set.
NEW QUESTION # 72
Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?
Answer: B
Explanation:
Option B is the correct fault-tolerance pattern for a long-running stateful workflow. A checkpoint captures enough durable execution state-completed steps, intermediate results, pending work, identifiers, and other necessary task state-to restart from a known consistent point rather than replaying the entire workflow after interruption.
This pattern is consistent with Claude's current stateful agent architecture. Anthropic's Managed Agents documentation describes persistent sessions that preserve conversation history across interactions. When a session becomes idle, its sandbox can be checkpointed so filesystem and execution artifacts are available when work resumes. The certification concept is broader than that specific hosted implementation: long- running agent systems should externalize recoverable state at meaningful boundaries.
A longer timeout does not protect against process crashes, infrastructure restarts, network failures, or deployment interruptions. C doubles resource consumption and creates consistency problems without providing deterministic recovery. D wastes completed work and may repeat external side effects.
Therefore, B provides controlled resumability and minimizes repeated processing. Relevant Study Guide topics: checkpointing, persistent state, resumable workflows, long-running agents, fault tolerance, idempotency, and recovery architecture.
NEW QUESTION # 73
You are setting up Claude Code for a new project repository. Your team has shared coding standards, preferred libraries, and project-specific context that every developer working on the repository should have available when they use Claude Code.
How would you set this up?
Answer: D
Explanation:
Option C is correct. Claude Code uses CLAUDE.md as the standard mechanism for supplying persistent project-level instructions and context. Anthropic's Claude Code documentation states that project instructions can be stored in ./CLAUDE.md or ./.claude/CLAUDE.md and shared with team members through source control. Appropriate content includes coding standards, architectural decisions, project conventions, build/test commands, preferred workflows, and information developers would otherwise need to repeat to Claude during every session. The /init command can also generate an initial CLAUDE.md based on the repository.
A wiki may be valuable for human documentation, but Claude Code does not automatically receive that material as project context. B creates developer-specific configuration and risks inconsistency across the team. D places the information in a human-facing README but does not use Claude Code's purpose-built persistent instruction mechanism.
Therefore, C gives the repository a shared, version-controlled source of Claude-specific project guidance.
Relevant Study Guide topics: CLAUDE.md, project configuration, persistent instructions, repository context, team standards, and configuration management.
NEW QUESTION # 74
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