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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Claude Agent SDK usage - Workflow vs autonomous agents - Memory and context management - Agent architecture principles |
| Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Applications and Integration | 33.1% | - SDK and third-party integration - Vision capabilities - Streaming and Batch API - Claude Messages API |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Prompt and Context Engineering | 11% | - Prompt design and structuring - Context window management - Structured output handling |
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NEW QUESTION # 33
Your Claude application's API keys are stored in a secrets manager. The team is debating whether the same key should be used in development, staging, and production environments.
How would you handle the keys across environments?
Answer: C
Explanation:
Option A provides proper environment isolation and credential blast-radius control. Development, staging, and production represent different trust boundaries and should not share the same API credential. If a development machine, CI job, or staging service is compromised, a distinct credential prevents the attacker from automatically gaining the same access to production.
Anthropic's official workspace documentation explicitly recommends using separate workspaces for development, staging, and production. Workspaces can have their own API keys, members, resource limits, and usage tracking, and keys can be scoped to a specific workspace. Anthropic's authentication guidance also instructs developers to store API keys in a secrets manager, rotate them periodically, revoke suspected compromised keys, and use workspaces to scope credentials by project or environment.
B confuses rotation with isolation: rotating a credential that remains shared across all environments does not create independent security boundaries. C deliberately increases blast radius. D merely substitutes one globally shared key for another and therefore has the same architectural flaw.
The supplied exam source marks A as correct. Relevant topics: Confia Management, secrets management, API-key scoping, environment isolation, credential rotation, workspaces, least privilege, and production security.
NEW QUESTION # 34
A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?
Answer: D
Explanation:
Option B is correct. A large prompt containing overlapping, contradictory, or obsolete instructions creates instruction ambiguity and increases the likelihood that Claude will apply an unintended rule, over-weight irrelevant guidance, or produce inconsistent behavior.
Anthropic's current prompting guidance reinforces this principle: Claude performs best when instructions are clear, explicit, and direct. Prompt design should minimize ambiguity and provide the model with precisely the context and behavioral requirements needed for the task. Refactoring should therefore remove obsolete requirements, consolidate duplicated instructions, resolve contradictions, and establish one authoritative formulation for each behavioral rule. The revised prompt should then be regression-tested against representative inputs.
A makes an already overloaded prompt larger and relies on instruction ordering instead of eliminating conflicts. C addresses only one defect while retaining redundant and obsolete material. D does not inherently solve contradictions and may fragment authoritative instructions across multiple locations.
Therefore, B addresses the root cause. Relevant Study Guide topics: system prompt design, prompt maintainability, instruction clarity, conflicting instructions, prompt refactoring, regression evaluation, and context quality.
NEW QUESTION # 35
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?
Answer: B
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 # 36
You are designing a multi-step Claude workflow where some steps must reason without seeing the full prior conversation history. The team wants to keep specific context isolated to specific steps.
The context engineering technique you would use is...
Answer: B
Explanation:
Option C is the correct application of context isolation. A specialized step should receive the minimum relevant information required for its own task rather than inheriting an ever-growing global transcript. This improves signal-to-noise ratio, limits accidental cross-task influence, controls token usage, and makes individual components easier to evaluate.
Anthropic's context-engineering guidance explicitly identifies multi-agent architectures as a technique for long-horizon work. Specialized subagents can operate with their own context windows and return condensed results to an orchestrating agent rather than exposing every agent to every intermediate detail. This architecture protects each reasoning process from irrelevant history while allowing the overall system to preserve necessary state.
Options A and D represent the opposite approach: indiscriminately loading the full prior history. Larger context is not automatically better; Anthropic warns that excessive context can introduce context pollution and degrade retrieval or attention to important information. B also fails because a single global prompt does not isolate state or tailor the information available to each processing stage.
Therefore, C best implements scoped reasoning boundaries. Relevant Claude Developer topics are multi-agent architecture, subagents, context isolation, context engineering, orchestration, and long-running workflow design.
NEW QUESTION # 37
You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?
Answer: D
Explanation:
Option B is the correct automation pattern. A CI pipeline must operate non-interactively, so Claude Code should be invoked in its programmatic/headless execution mode and given only the permissions required for the predefined workflow. Output can then be captured by the pipeline and supplied to subsequent build, testing, reporting, or deployment stages.
Claude Code's configuration system supports explicit permission rules in JSON settings. Anthropic documents that settings.json controls, among other behavior, what Claude Code can execute without asking.
For example, permissions.allow can authorize specific commands while permissions.deny prevents sensitive operations. Project-level .claude/settings.json can be version-controlled so CI behavior is reproducible across runners.
This is substantially safer than C, which disables confirmation behavior globally and expands the blast radius beyond the CI workload. D defeats automation because a human must remain present. A is unnecessary because Claude Code is explicitly configurable for automation scenarios.
The principle is least-privilege non-interactive configuration: authorize only the commands and resources required by the pipeline and capture machine-readable output for downstream processing. Relevant Claude Developer topics are Claude Code automation, configuration management, permissions, CI/CD integration, headless execution, and reproducible project settings.
NEW QUESTION # 38
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