CCDV-F Practice Test 2 — 25 Questions

Question 1

You are designing a multi-agent system where a 'Router' agent directs tasks to a 'Research' agent or a 'Writer' agent. What is the most efficient way to maintain state when the Research agent completes its task and needs to hand back control?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Appending the Research agent's tool results to the main message thread.. Handoffs in multi-agent systems rely on passing the conversation history or a summarized state between specialized instances. This ensures that the Router has the necessary context to decide the next step without losing the progress made by the Research agent. Proper state management prevents redundant work and keeps the agentic flow aligned with the user goal.

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Persisting the Research agent's output to a global database for the Router.

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Restarting the entire session with the Research output as a new prompt.

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Using a separate API key for each agent to isolate their environments.

Question 2

A security auditor is reviewing an application that uses the Model Context Protocol (MCP). Which TWO security considerations are most important for the developer to address when implementing the MCP Server?

Accepted Answer

The server should strictly validate all tool arguments against its internal logic.. Security in MCP is a shared responsibility. While the protocol facilitates communication, the server developer must ensure that the tools and resources exposed do not create vulnerabilities. This includes limiting access to the file system and ensuring that tool inputs are properly validated before being used in potentially dangerous operations like shell commands.

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The server must use a unique API key for every tool call it receives.

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The server must rotate its connection ID every sixty seconds for privacy.

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All MCP tool results must be manually approved by a human operator.

Question 3

When implementing a 'Computer Use' agent, which THREE capabilities are provided by the standardized Anthropic beta tools 'computer', 'text_editor', and 'bash'?

Accepted Answer

Capturing screenshots and performing mouse/keyboard interactions.. Anthropic provides specialized tools for computer interaction to ensure consistent behavior across different environments. These tools allow the agent to perform GUI actions, modify files with precision, and execute shell commands. Together, they form a comprehensive suite for agents that need to operate as a virtual developer or assistant on a computer system.

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Rewriting entire binary files to optimize system performance.

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Automatically upgrading the host operating system to the latest version.

Question 4

Refer to the exhibit. A developer is implementing the provided JSON structure to optimize an application that repeatedly analyzes the same large report. What is the primary financial implication of using the 'cache_control' block in this specific API request?

Accepted Answer

Subsequent requests with the same report will be billed at a reduced rate.. The 'cache_control' block enables Prompt Caching, which is a key tool for cost management. By marking a block as ephemeral, Anthropic caches the preceding content. If the same content is sent again within the cache's lifetime, the developer is charged a significantly lower 'cache hit' rate instead of the full input token price. This is essential for reducing costs in applications with static contexts.

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It eliminates the cost of the first 1024 tokens in every request.

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It triggers a 50% discount on all output tokens for the session.

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The request will be processed using the Batch API pricing model.

Question 5

In the context of Anthropic's pricing model, why is it generally recommended to provide only the necessary context rather than the entire available dataset in a single prompt?

Accepted Answer

To minimize the input token count and keep the per-request cost low.. Token-based pricing means that every piece of information sent to the model has a direct financial cost. Large prompts not only increase the bill but can also lead to 'context stuffing,' which might degrade the model's focus. Efficient prompt engineering—sending only what is needed—is the fundamental practice for both cost management and ensuring high-quality, relevant responses from the AI.

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Because Anthropic charges a 'search fee' for every 1,000 tokens of context.

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Because Claude models cannot process more than 10,000 tokens at a time.

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To prevent the model from reaching its daily 'knowledge limit'.

Question 6

A developer needs to estimate the monthly budget for a new internal knowledge base application powered by Claude. Which TWO factors directly influence the total token consumption and subsequent cost of the API requests?

Accepted Answer

The total number of input tokens in the prompt. Estimating costs requires understanding the components of the billing model used by Anthropic. Total cost is derived from the volume of input tokens sent to the model and the volume of output tokens generated by the model. Developers must account for both segments because they are priced at different rates across the Claude 3 model family to reflect processing requirements.

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The number of concurrent users accessing the application

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The physical geographic location of the application server

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The programming language used to make the API calls

Question 7

Which THREE of these actions are considered safe and standard for Claude Code to perform during an interactive session?

Accepted Answer

Reading and summarizing codebase files.. Claude Code is built with safety as a priority. It is expected to handle file editing, command execution, and code review as part of its normal operation. Understanding these standard actions allows developers to trust the agent's output and integrate it into their workflow efficiently. By focusing on safe, verified, and transparent interactions, the agent maintains its role as a helpful assistant while minimizing risks associated with uncontrolled automation in a local project.

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Deleting arbitrary user files without asking.

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Exfiltrating environment variables to remote servers.

Question 8

An agent developer is building a workflow where an agent must query a private database. To ensure the agent only performs read operations, which component of the Anthropic Agent SDK pattern should be prioritized?

Accepted Answer

A strictly defined tool schema limited to read-only functions.. Tool definitions are the primary mechanism for restricting agent capabilities. By explicitly defining the schema and purpose of the tool, the developer enforces the principle of least privilege. The agent only executes what is provided in the tools list, making it vital to sanitize inputs and restrict access permissions at the API level rather than relying solely on natural language instructions within the system prompt.

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A complex system prompt with strict negative constraints.

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The Agent SDK's built-in session state management.

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The model's internal safety filtering layer.

Question 9

A developer wants to reduce latency and costs for a high-traffic application that sends a large, static set of instructions in every request. Which API feature should they implement to achieve this?

Accepted Answer

Prompt Caching using cache_control.. Prompt Caching is a specialized feature designed to optimize performance for repetitive content. By marking specific parts of the prompt as cacheable, developers can significantly reduce the processing time for the 'prefill' phase and take advantage of lower pricing for cached tokens. This is particularly effective for large system prompts or complex context documents.

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Batch API processing.

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Top-k sampling reduction.

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System prompt compression.

Question 10

Which TWO of the following are essential components of a 'tool_use' block generated by Claude?

Accepted Answer

A unique 'id' string used to match results to the specific request.. A 'tool_use' block must contain specific identifiers so that the SDK knows which tool to run and how to track its result. The 'id' uniquely identifies the specific call in the conversation history, while the 'name' maps the call to the actual function or service defined in the agent's configuration.

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The full source code of the function being called.

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The 'timestamp' of when the model decided to use the tool.

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The 'confidence_score' the model assigns to this specific tool call.

Question 11

Which of the following scenarios describes the most effective use of Prompt Caching for cost management?

Accepted Answer

An AI assistant that references a 50-page technical manual for every user query.. Prompt caching is most effective when a large amount of static information is reused across many different requests. It allows the model to 'remember' the prefix of a prompt, significantly reducing the cost of processing that prefix in subsequent calls. Identifying workloads with high prefix overlap is key to maximizing the financial benefits of this feature in production environments.

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A chatbot where every user query is unique and no history is maintained.

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A translation service that processes single words one at a time.

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A daily report generator that uses a completely different dataset every morning.

Question 12

Refer to the exhibit. An application monitoring system captures this response from the Anthropic API. Which strategy is the most mechanically sound approach for the application to take to resolve this specific error and continue processing?

Accepted Answer

Implementing an exponential backoff algorithm before retrying the request.. Rate limit errors occur when a developer exceeds the allocated requests per minute or tokens per minute for their tier. Handling these gracefully via exponential backoff is a fundamental skill for API integration. This approach ensures that the application doesn't overwhelm the server further, allowing the rate limit bucket to refill naturally while maintaining the best possible user experience.

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Immediately resending the request with a new API key to bypass the limits.

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Switching the model parameter to a larger model like Claude 3 Opus.

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Increasing the max_tokens parameter to ensure the request is prioritized.

Question 13

An agent using the SDK encounters an error where it generates a tool call for a tool that does not exist in its configuration. Which adjustment to the agent's setup is most likely to resolve this hallucination?

Accepted Answer

Refining the system prompt to clearly define the boundaries of available tools.. Hallucinating non-existent tools usually occurs when the model is aware of a capability but hasn't been given the structured definition for it, or when the system prompt is ambiguous. Refining the system prompt to explicitly list the available tools and their purposes helps ground the model's reasoning in the provided technical context.

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Enabling 'force_tool_use' to ensure it only uses one of the existing tools.

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Switching the model from Claude 3.5 Sonnet to Claude 3 Haiku.

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Increasing the max_tokens parameter to allow for more reasoning.

Question 14

A developer wants to integrate Claude Code into a CI/CD pipeline. Why is this generally discouraged or difficult to achieve directly?

Accepted Answer

The tool is designed for interactive human sessions.. Claude Code is primarily an interactive, human-in-the-loop tool. Its design relies on terminal input, natural language prompts, and real-time user validation, which are not conducive to non-interactive CI/CD automation. CI/CD pipelines require deterministic, automated scripts. Attempting to force an interactive agent into such environments leads to unpredictable behavior, race conditions, and high costs, as the agent is not optimized for the standard, scripted environment of automated build and deployment systems.

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The tool is not licensed for use in CI/CD pipelines.

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The tool requires an internet connection for every step.

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It cannot execute shell commands reliably.

Question 15

A developer is concerned about the 'token overhead' when using system prompts. How does Anthropic charge for tokens included in the system prompt compared to tokens in the user message?

Accepted Answer

They are billed at the same standard input token rate as the user message.. Understanding the billing of different prompt components is essential for cost management. Anthropic treats all input tokens equally, regardless of their role in the message (system, user, or assistant). This means that a large system prompt containing complex instructions or few-shot examples will incur the same per-token cost as the actual user query or any historical context provided in the conversation.

Answer

System prompt tokens are billed at a premium rate because they influence the model more.

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System prompt tokens are free for the first 500 tokens in every request.

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System prompts are only billed if they exceed the 200,000-token context limit.

Question 16

A developer is building a Claude-based agent that uses tool calling to interact with a database. During testing, the agent executes a tool that deletes records when the user's prompt contains the phrase 'clean up old data'. The developer wants to prevent unintended destructive actions while still allowing the agent to propose deletions. Which design change is most effective?

Accepted Answer

Implement a human-in-the-loop approval step where any tool call that deletes data requires explicit user confirmation before execution.. The most effective change is to add a human-in-the-loop approval step for destructive tool calls. This enforces a hard control that prevents unintended deletions regardless of model behavior, while still allowing the agent to propose actions and a human to confirm them.

Answer

Use a more powerful model with better reasoning to avoid misinterpreting the prompt.

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Limit the database user's permissions to read-only, so the delete tool will fail if called.

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Add a system prompt instruction telling Claude to never delete data without explicit user confirmation.

Question 17

You are building a support-triage assistant on the Anthropic API. Each request must return a JSON object with keys 'category' and 'priority'. During testing, Claude wraps its output in markdown fences and adds a friendly sentence before the JSON. You want the raw, parseable object every time without changing the model or adding a second call. What is the most reliable prompt-level change?

Accepted Answer

Pre-fill the assistant turn with an opening brace so the response must continue as the JSON object.. Constraining the assistant turn with a leading brace forces continuation as the JSON object, since the model must complete the already-started structure. This removes markdown fences and conversational preamble in a single call, with no model change. Instruction-only or explanation-first approaches leave room for stray text, so prefilling is the most reliable prompt-level fix for guaranteed parseable output.

Answer

Ask Claude to explain its reasoning first, then append the JSON at the end of the response.

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Add the sentence 'Please output only JSON' to the system prompt and rely on that instruction alone.

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Increase the temperature setting so the model has more freedom to choose a clean format.

Question 18

A developer is defining a tool for Claude that fetches the current stock price for a ticker symbol. The tool will be called `get_stock_price`. Which `input_schema` definition best enables Claude to call the tool correctly?

Accepted Answer

A JSON Schema object with `type: "object"`, a `properties` entry for `ticker` of `type: "string"`, and a `required` array containing `"ticker"`.. Tool definitions should describe inputs as a JSON Schema object with named, typed properties and a `required` array. For a stock lookup, a required string `ticker` tells Claude exactly what to supply. Permissive schemas, non-object root types, or missing required/description fields all degrade the model's ability to produce correct tool call arguments, so the fully specified object schema is the right choice.

Answer

A JSON Schema object with `type: "object"` and a free-form `additionalProperties: true` allowing any fields.

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A JSON Schema object with `type: "object"` and a `properties` entry for `ticker`, but no `required` array and no `description`.

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A JSON Schema object with `type: "string"` and a `description` explaining that the value should be a ticker symbol.

Question 19

A developer is implementing a multi-agent system using the Anthropic SDK where a 'Coordinator' agent delegates tasks to specialized 'Researcher' and 'Writer' agents via tool calls. The Coordinator must maintain a coherent conversation state and avoid redundant work. Which TWO of the following practices are essential for the Coordinator to effectively manage this delegation? (Choose two.)

Accepted Answer

Include the full conversation history, including all tool_use and tool_result blocks, in each subsequent request to the Coordinator so it can reason about prior delegations.. Effective multi-agent coordination with the Anthropic SDK requires maintaining the full conversation history to provide context, and defining clear, well-described tools for each specialized agent. These practices enable the Coordinator to make informed delegation decisions and avoid redundancy. Other options are either not essential or potentially harmful.

Answer

Set the `temperature` to 0 for the Coordinator to ensure deterministic routing decisions.

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Implement a separate memory store (e.g., a database) that the Coordinator queries before each delegation to check if the task was already completed.

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Use the `max_tokens` parameter to limit the Coordinator's response length, ensuring it does not generate overly long delegations.

Question 20

A developer is designing an agent with the Anthropic SDK that must follow strict brand guidelines when drafting customer emails. The agent should never deviate from these rules regardless of how the user phrases a request. Where should the brand guidelines be placed to have the strongest effect?

Accepted Answer

In the system prompt, so they act as persistent instructions across the entire conversation.. The system prompt is the correct place for durable rules that must apply to every response, such as brand guidelines. It carries more weight than user turns and persists across the conversation, making it the strongest lever for preventing deviations regardless of how a request is phrased.

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In the assistant's previous reply, so the model imitates its own earlier tone.

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In the first user message, so the model sees them before any other request.

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In a tool description, so the model reads them whenever it considers using a tool.