CCDV-F Practice Test 2 — 30 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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.

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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 11

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.

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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 12

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.

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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 13

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.

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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 14

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.

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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 15

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.

Question 16

A developer wants Claude to return a fixed set of user-profile fields extracted from free-text bios. They want the response to be valid JSON they can parse without regex. The model occasionally adds a friendly sentence before the JSON. Which Messages API feature most reliably constrains the output?

Accepted Answer

Define a tool whose input_schema describes the profile fields, then set tool_choice to force Claude to call it.. Structured extraction is best enforced by defining a tool whose input_schema specifies the exact fields and types, then using tool_choice to require that the model call it. Claude then returns a tool_use block whose input is JSON matching the schema, eliminating stray prose. Prompt wording, temperature, stop sequences, and token limits influence style or length but cannot enforce a schema.

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Increase max_tokens so Claude has more room to format the JSON correctly.

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Set stop_sequences to "}" so generation halts exactly at the end of the JSON object.

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Add the word "JSON" to the prompt and set temperature to 0.

Question 17

A team is building a retrieval-augmented assistant that sends a large document set inside the system prompt on every turn. To reduce cost, they enable prompt caching. They notice cache_read_input_tokens is high on most turns but intermittently drops to zero even though the system prompt text has not changed. Which explanation best fits this behavior?

Accepted Answer

The cache has a short time-to-live, so if enough time passes between requests the cached prefix expires and the next request pays full input cost before re-populating the cache.. Prompt caching entries expire after a time-to-live if they are not referenced. Bursty or idle traffic patterns cause the prefix to be evicted between requests, so the next call misses the cache and reports zero cache reads before re-creating the entry. Keeping steady traffic, or reducing gaps between calls, stabilizes cache_read_input_tokens and preserves the intended cost savings.

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The API randomly invalidates cache entries per account to distribute load, so intermittent cache misses are expected and cannot be controlled.

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Prompt caching only applies to user messages, so any content placed in the system prompt bypasses the cache and is billed at full input rate.

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Cache reads only register when max_tokens is large enough to cover the entire cached prefix, so smaller responses skip the cache.

Question 18

A developer is building a support agent with the Claude Agent SDK. The agent must pause execution, ask a human operator to approve a refund above $500, and resume the exact same session with the operator's decision injected as a new message. Which SDK capability should the developer configure to accomplish this reliably?

Accepted Answer

Permission callbacks combined with session persistence, so the agent suspends on the sensitive action and later resumes from the saved session state.. Approval gates require an enforcement point plus durable state. Permission callbacks intercept the sensitive tool call before it executes, and session persistence stores the conversation so the run can be resumed later with the operator's decision appended. Together they suspend and continue the same session deterministically, which prompt instructions, blocking handlers, or retry loops cannot provide.

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Wrapping the refund tool in a retry loop that re-invokes the model until the operator approves in a separate chat window.

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A custom tool whose handler blocks on stdin and returns the operator's typed answer as a tool_result.

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Increasing max_tokens and instructing the model in the system prompt to ask for confirmation before calling the refund tool.

Question 19

A developer is building a customer-support agent using the Anthropic API with the Claude 3.5 Sonnet model. The agent exposes a `lookup_order` tool that takes an `order_id` string. During testing, a user asks 'Where is my order 88213?' and Claude returns a `tool_use` block with `name: "lookup_order"` and `input: {"order_id": "88213"}`. What must the developer send back to Claude in the next API request so it can produce a natural-language answer?

Accepted Answer

A `user` message containing a `tool_result` content block whose `tool_use_id` matches the `tool_use` block and whose `content` holds the lookup result.. After Claude emits a `tool_use` block, the developer must return a user message containing a `tool_result` block that carries the same `tool_use_id`. This pairing is how the model links its request to the returned data. Once the matching result is provided, Claude can generate the final answer about the order. Any other message shape leaves the tool call unsatisfied and prevents a grounded response.

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A new `system` message that contains the order details and instructs Claude to answer the user.

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An `assistant` message that echoes the `tool_use` block and appends the lookup result inside the same block.

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A `user` message containing the raw JSON result of the order lookup, with no `tool_use_id` reference.

Question 20

A developer's agent calls a weather API tool but Claude keeps emitting the tool call with a misspelled parameter name that the API rejects. The tool definition and the API schema disagree. What is the most likely cause?

Accepted Answer

The tool's input_schema does not accurately describe the parameter names and types the handler expects.. Tool inputs are generated from the declared input_schema, so any divergence between that schema and the handler's real expectations surfaces as malformed arguments. The model is not guessing; it is following the contract it was given. Correcting the schema to match the API and handler resolves the misspelled or mistyped parameter problem at its source.

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The model's temperature is set too low, causing it to repeat an earlier mistake.

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The system prompt does not explicitly forbid the model from inventing parameter names.

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The tool_result returned to the model is too long, so the model forgets the correct parameter names.