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| Section | Weight | Objectives |
|---|---|---|
| Prompt and Context Engineering | 11% | - Context Engineering
|
| Agents and Workflows | 14.7% | - Agent Architecture and Tradeoffs
|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
| Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
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NEW QUESTION # 30
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: A
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 # 31
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?
Answer: C
Explanation:
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.
NEW QUESTION # 32
Your team is choosing how to add a capability to a Claude application. You want to apply the appropriate option, whether built-in tool, custom tool, Skill, or MCP server, based on the use case.
You would choose the option that...
Answer: C
Explanation:
Option D reflects the correct architecture-selection principle: extension mechanisms should be selected according to what the capability must accomplish rather than familiarity, novelty, or implementation convenience. Anthropic explicitly distinguishes Claude extension mechanisms by purpose. Built-in tools cover common capabilities already supported by the platform. Custom tools expose application-specific callable operations through defined schemas. Skills package reusable knowledge, instructions, and workflows.
MCP connects Claude to external systems, APIs, databases, and services through a standardized protocol.
The decision therefore depends on scope and integration boundaries. A reusable procedural workflow may belong in a Skill; access to an external enterprise system may warrant MCP; a narrowly application-specific operation can be a custom tool; and a built-in tool should generally be preferred when it already satisfies the requirement.
A makes prior team experience the architectural criterion rather than requirements. B assumes newer technology is inherently better. C optimizes implementation convenience without considering maintainability, interoperability, or reuse.
Therefore, D is the appropriate selection rule. Relevant Study Guide topics: built-in tools, custom tools, Skills, MCP, extension architecture, reuse boundaries, and capability selection.
NEW QUESTION # 33
Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?
Answer: B
Explanation:
Option B applies few-shot, or multishot, prompting, one of Anthropic's recommended techniques for steering Claude when examples of desired behavior are available. Labeled input/output pairs give Claude concrete demonstrations of how it should respond, which is particularly valuable when edge cases are difficult to express completely through abstract rules.
Anthropic states that examples are among the most reliable mechanisms for steering output format, tone, and structure. Its prompting guidance recommends relevant, diverse examples that cover edge cases while avoiding accidental patterns. For best results, examples should be clearly separated from the main instructions, such as by using < example > and < examples > tags.
A retrieval database could be useful if a very large or dynamically selected example collection were required, but that adds unnecessary complexity for the small labeled set described. C is disproportionate: a few examples do not justify replacing the application's Claude integration with custom model training. D avoids rather than solves the identified failure mode.
Therefore, B directly uses the available supervision at inference time and allows rapid iteration through evaluation. Relevant Claude Developer topics are prompt construction, few-shot prompting, edge-case handling, example selection, evaluation-driven iteration, and behavioral steering.
NEW QUESTION # 34
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: B
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 # 35
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