CCDV-F Agents and the Agent SDK Practice Test 3 — 15 Questions

Question 1

A developer is building a Claude agent using the Anthropic Agent SDK. The agent must be able to fetch the current stock price for a given ticker symbol. The developer defines a tool named 'get_stock_price' with an input schema that requires a single string parameter 'ticker'. During testing, the agent responds with a final text answer containing a plausible-looking price, but the tool is never invoked. Which is the most likely cause?

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

Accepted Answer

The tool description field is missing or too vague, so the model does not understand when to use the tool and instead answers from its own knowledge.. The model selects tools based on their name and description. A missing or vague description means the model lacks the information needed to connect the user's request to the tool, so it answers from its own knowledge instead of calling the tool. Providing a clear, specific description that explains what the tool does and when to use it is critical for reliable tool invocation in the Anthropic Agent SDK.

Answer

The tool's JSON schema uses 'type': 'string' for the 'ticker' parameter, but the agent expects an 'enum' of valid tickers.

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The tool's input schema declares 'ticker' as required, but the model cannot provide a value because the user did not specify a ticker symbol.

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The agent's system prompt does not include the phrase 'You must use tools when available', so the model ignores the tool.

Question 2

A developer is building a Claude agent with the Anthropic SDK that must look up live order status. The agent has one tool named get_order_status, and its input_schema declares a required string property order_id. During testing, Claude returns a tool_use block with name "get_order_status" and input {"order_id": "A-4471"}. What must the developer's application do next to keep the agent loop running correctly?

Accepted Answer

Execute the lookup, then send a user-role message containing a tool_result block with the matching tool_use_id back to Claude.. The agent loop alternates model reasoning with client-side tool execution. When Claude emits a tool_use block, the developer's code runs the named tool and returns the output as a tool_result content block in a user message, keyed by the same tool_use_id. That correlation lets Claude pair the result with its request and produce the final answer using real order data.

Answer

Call the tool, then send the raw JSON output as a plain assistant text message so Claude can read the order details directly.

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Append Claude's tool_use block to the conversation and immediately call the Messages API again with the same parameters to obtain the final answer.

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Convert the tool_use block into a system message so the model treats the tool request as persistent instructions for the rest of the session.

Question 3

A developer is building a customer support agent using the Anthropic SDK. The agent must call a 'get_order_status' tool that requires an order ID. During testing, the agent sometimes invents plausible-looking order IDs instead of asking the user. Which change to the tool definition will most directly reduce this behavior?

Accepted Answer

Add a detailed 'description' field to the tool that explains when to use it and explicitly instructs the model not to guess missing parameters.. The tool description is the model's instruction manual for when and how to invoke a tool. By explicitly stating that the agent must request an order ID from the user rather than guessing, the developer directly targets the observed hallucination. Schema changes, token limits, and renaming do not convey that behavioral rule.

Answer

Increase the 'max_tokens' setting so the model has more room to reason before emitting the tool_use block.

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Set 'input_schema' to an empty object so the model is forced to ask the user for all parameters.

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Change the tool name to 'lookup_order' so it sounds more like a retrieval operation.

Question 4

A developer is building an agent using the Anthropic Python SDK. The agent's system prompt instructs it to answer questions about a company's internal policies by searching a vector database. The developer wants the agent to autonomously decide when to search and when to answer directly. Which combination of API features should the developer implement to achieve this?

Accepted Answer

Define a tool that queries the vector database, include it in the `tools` parameter of the Messages API request, and handle `tool_use` blocks in the response by executing the search and returning a `tool_result` block.. The correct approach is to define the vector search as a tool, include it in the request, and handle the tool_use/tool_result cycle. This enables the model to decide when to search based on the conversation, which is the core of agentic behavior with the Anthropic SDK. Other options either force tool use, embed excessive context, or bypass the model's decision-making entirely.

Answer

Use the `system` parameter to embed the entire vector database content as context, allowing the model to answer directly without tool use.

Answer

Implement a pre-processing step that always queries the vector database and appends the top result to the user's message before sending it to the model.

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Set the `tool_choice` parameter to `{"type": "any"}` so the model always calls the vector search tool, and then parse the result to generate the final answer.

Question 5

An agent built with the Anthropic SDK is processing a long conversation and begins to exceed the model's context window. The developer wants to preserve recent turns and key facts while reducing token usage. Which approach best matches how the SDK and Claude are designed to handle this?

Accepted Answer

Summarize older portions of the conversation into a compact message and prepend it, keeping the most recent turns verbatim.. Because the API is stateless, context management is the developer's responsibility. Summarizing older conversation segments while retaining recent turns verbatim balances token economy with continuity. This pattern is widely used in long-running agents to stay within the context window without losing the facts that matter.

Answer

Rely on the model's built-in long-term memory to automatically recall earlier turns without resending them.

Answer

Increase the temperature setting so the model can compress the conversation on its own.

Answer

Remove all prior messages and send only the latest user turn to avoid any risk of exceeding the limit.

Question 6

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 7

A developer is building an agent that must decide when to call tools versus when to respond directly to the user. The agent uses the Anthropic SDK with tool definitions supplied in the request. Which TWO behaviors correctly describe how the model handles tool use in this setup? (Choose two.)

Accepted Answer

The model can return a normal text response instead of a tool_use block when it determines no tool is required.. Claude decides whether to call a tool and, if so, emits a tool_use block with the name and arguments; it may also answer directly with text when no tool is needed. The application, not the model, performs execution and returns results, which is why the agent loop must handle both tool_use and plain text outcomes.

Answer

The model requires the tool's input_schema to be omitted so it can choose parameters freely.

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The model automatically retries a failed tool call until it succeeds without developer intervention.

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The model executes the tool itself and returns the result directly in the same response.

Question 8

A developer is creating an agent that uses the Anthropic SDK to answer customer support questions. The agent has access to a tool that retrieves order status from an internal API. During testing, the developer notices that the agent sometimes provides an order status without calling the tool, resulting in incorrect information. What is the most likely cause of this behavior?

Accepted Answer

The tool's description is too vague, so the model does not understand when it should be used.. The most likely cause is a vague tool description. The model uses the description to understand the tool's purpose and when to invoke it. If the description does not clearly explain that the tool retrieves order status, the model may answer from its own knowledge, leading to incorrect information. Improving the description will guide the model to use the tool appropriately.

Answer

The agent's system prompt does not include the current date, so the model cannot determine order freshness.

Answer

The `tool_choice` parameter is set to `{"type": "auto"}`, which allows the model to ignore tools.

Answer

The model's temperature is set too high, causing it to hallucinate tool calls.

Question 9

A developer writes an agent loop that handles Claude's tool_use blocks by executing each tool and appending a user message containing the tool_result blocks. In production, a request occasionally triggers a 400 error stating that the tool_use ids were not found. Reviewing the code, the developer sees the loop builds each new request by sending only the latest user message and the newest tool_result message, discarding earlier turns to save tokens. Which action resolves the error while preserving the agent's behavior?

Accepted Answer

Send the complete conversation history on every request, including the assistant turn containing the tool_use blocks that the tool_result messages reference.. Because the Messages API holds no server-side session, every request must include the prior assistant turn that emitted the tool_use blocks alongside the tool_result messages that cite those ids. Trimming history to save tokens removed the referenced blocks, so the API rejected the orphaned results. Sending the complete history, or a window that retains each referenced tool_use turn, restores valid correlation.

Answer

Generate a new UUID for each tool_result block and set the tool_use_id field to that fresh value so the results appear unique to the API.

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Switch the loop to the Batches API so that multi-turn tool exchanges are processed together and prior messages are retained on the server.

Answer

Reduce the number of tools passed in the tools parameter so that fewer tool_use blocks are generated and fewer ids need to be matched.

Question 10

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.

Answer

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

Answer

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

Answer

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

Question 11

A developer is using the Anthropic SDK to build an agent that can answer questions by calling external APIs. The agent's tool returns a large JSON payload (over 100,000 tokens) as a `tool_result`. The developer notices that subsequent requests to the model fail with a context window error. What is the most effective way to resolve this issue?

Accepted Answer

Truncate or summarize the tool result before including it in the conversation history.. The most effective solution is to truncate or summarize the tool result before adding it to the conversation history. The context window includes all input tokens, so a massive tool result will cause errors. By reducing the size, the developer ensures the conversation stays within limits. Other options do not address the root cause of the oversized input.

Answer

Increase the `max_tokens` parameter in the request to accommodate the large tool result.

Answer

Set the `tool_choice` parameter to `{"type": "none"}` to prevent the model from calling the tool again.

Answer

Switch to a model with a larger context window, such as Claude 3.5 Sonnet, without modifying the tool result.

Question 12

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.

Answer

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

Answer

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

Answer

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

Question 13

A developer is creating a multi-agent system where a coordinator agent delegates subtasks to specialized agents. Each specialized agent has its own tools and system prompt. The coordinator needs to know when a subtask is complete and what result was produced. Which design best fits the Anthropic SDK's stateless model?

Accepted Answer

Have the coordinator invoke each specialized agent as a tool, passing the subtask as input and receiving the agent's final result as the tool output.. Because each API call is independent, delegation is best modeled as a tool call: the coordinator requests a sub-agent by name, the application runs it and returns the result as tool output. This creates an explicit contract for completion and results, and it keeps each specialized agent's tools and system prompt isolated.

Answer

Have the coordinator and specialized agents share a single conversation history that all agents append to directly.

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Have all specialized agents run in parallel and write their outputs to a shared memory store that the model reads automatically.

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Have the coordinator emit a plain text message describing the subtask and rely on the specialized agent to detect it by polling.

Question 14

An agent built with the Claude Agent SDK runs a long research task and repeatedly hits the model's context window limit. The developer wants the agent to keep working without losing critical earlier findings. Which approach best addresses this?

Accepted Answer

Enable automatic context compaction so older turns are summarized into a condensed form while key findings are retained in the running context.. Long-running agents need a strategy for the finite context window. Compaction condenses older turns into summaries or retained highlights, freeing tokens while keeping the findings the agent depends on. Sampling parameters and response-length caps do not reclaim history, and restarting loses state, so compaction is the mechanism that lets the run continue coherently.

Answer

Raise the temperature so the model generates shorter responses and consumes fewer tokens per turn.

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Restart the agent from scratch each time the limit is reached, relying on the model to re-derive earlier findings from the task prompt.

Answer

Increase the model's max_tokens parameter so each response can be longer and the context window is used more efficiently.

Question 15

A developer is designing a subagent architecture with the Claude Agent SDK. The main agent delegates a codebase audit to a specialized subagent to keep the primary conversation focused. Which TWO practices help ensure the delegation works correctly? (Choose two.)

Accepted Answer

Give the subagent a narrowly scoped task description and only the tools it needs for the audit.. Effective delegation depends on isolation and distillation. Scoping the subagent's task and tools keeps it focused and safe, while returning a concise structured summary preserves the main agent's context budget and attention. Sharing full history, granting broad tools, or dumping raw logs all reintroduce the noise that subagents are meant to filter out.

Answer

Configure the subagent with the same broad toolset as the main agent to maximize flexibility.

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Have the subagent write its raw logs directly into the main agent's message history for transparency.

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Share the main agent's entire conversation history with the subagent so it has maximum context.