CCDV-F Model Selection and Cost Management Practice Test 3 — 15 Questions

Question 1

An AI engineering team is auditing an expensive Claude API integration processing millions of customer support queries daily. Which TWO strategies should they implement to effectively reduce token expenditure without sacrificing core model intelligence? (Choose two)

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

Accepted Answer

Implement Anthropic prompt caching for large, recurring system prompts and reference knowledge bases.. Optimizing context management and leveraging prompt caching are cornerstone strategies for cost reduction in high-volume Anthropic integrations. Caching static system prompts drastically lowers input token charges across repeated requests, while concise prompts eliminate redundant verbiage. Together, these practices protect infrastructure budgets while sustaining the semantic fidelity required by enterprise-grade support workflows.

Answer

Switch every production workload immediately from Claude 3.5 Sonnet to Claude 3 Opus to maximize parallel processing.

Answer

Disable response streaming to batch full payloads and reduce HTTP connection overhead.

Answer

Hardcode maximum output tokens to five thousand across all API calls regardless of task complexity.

Question 2

A developer runs a nightly batch job that sends 8,000 independent product-description summarization requests to the Anthropic API. Each request shares an identical 12,000-token instruction block and a unique 300-token product description. The team wants to reduce input-token spend without changing output quality or model. Which approach best meets this goal?

Accepted Answer

Use prompt caching with cache_control on the shared 12,000-token instruction block and place it at the beginning of each request.. The dominant cost is the repeated 12,000-token instruction block, not the small per-product text. Prompt caching is designed for exactly this pattern: a stable, large prefix reused across many calls. Marking that block with cache_control lets later requests read it at the reduced cache-read rate, while the variable 300-token description remains uncached. This reduces spend without changing the model, the instructions, or the output quality.

Answer

Send all 8,000 product descriptions in a single request so the instruction block is transmitted only once.

Answer

Switch the batch job to a smaller, cheaper model for all 8,000 requests.

Answer

Reduce the shared instruction block to 2,000 tokens so that every request costs less per call.

Question 3

You are building a summarization feature that processes 20,000 support tickets nightly. Each ticket is under 4,000 tokens and the output summary is around 300 tokens. The job must complete within a 6-hour window and you want to minimize cost. Which Claude model should you choose?

Accepted Answer

Claude 3 Haiku. For high-volume, short-input summarization where cost is the primary constraint, Claude 3 Haiku provides the best balance of speed, quality, and price. Its lower per-token cost directly reduces the total spend for 20,000 requests, and its speed helps meet the 6-hour window. More expensive models like Sonnet or Opus are overkill for this task.

Answer

Claude 3 Sonnet

Answer

Claude 3 Opus

Answer

Claude 3.5 Sonnet

Question 4

A developer is optimizing a Claude-powered chatbot that handles 50,000 daily conversations. Each conversation starts with a 1,200-token system prompt that includes detailed instructions and examples. The system prompt is identical across all conversations. The developer wants to reduce input token costs. Which technique should be applied?

Accepted Answer

Enable prompt caching for the system prompt.. Prompt caching is designed for scenarios where a large, static prefix is reused across many requests. By caching the 1,200-token system prompt, the developer pays full price only once, and subsequent requests benefit from reduced input token costs. This directly targets the repetitive cost without sacrificing quality or altering the conversation flow.

Answer

Shorten the system prompt by removing examples.

Answer

Move the system prompt to the first user message.

Answer

Switch to a cheaper model for all conversations.

Question 5

A developer is using the Claude Messages API to generate a 500-token response. The input consists of a 200-token user message and a 100-token system prompt. Which factor directly determines the output token cost of this API call?

Accepted Answer

The number of tokens in the model's response.. Output token cost is based exclusively on the number of tokens the model generates in its response. In this case, the 500-token response defines the output cost. Input tokens, such as the system prompt and user message, are billed separately as input tokens. Therefore, the response length is the direct determinant of output token cost.

Answer

The number of tokens in the user message.

Answer

The number of tokens in the system prompt.

Answer

The total number of tokens in the request (input plus output).

Question 6

A developer is building a Claude-powered coding assistant that must answer questions about a 60,000-token proprietary codebase on every user request. The codebase is static and updated only weekly. The developer wants to minimize per-request input token costs while keeping latency low. Which approach is MOST cost-effective?

Accepted Answer

Enable Prompt Caching on the codebase context so subsequent requests reuse the cached prefix at a reduced input token rate.. Prompt Caching is designed exactly for repeated large static prefixes such as a codebase that changes infrequently. Cache reads are billed at a lower rate than standard input tokens, and the cache persists for a short TTL, so a weekly update cadence yields very high hit rates. The other approaches either discard necessary context or rely on unsupported mechanisms.

Answer

Use the smallest available model and hope it can reason about the codebase without additional context.

Answer

Send only the file names and ask Claude to infer the implementation details from the names.

Answer

Compress the codebase using a custom tokenizer before sending it to the Messages API.

Question 7

A developer is building a real-time translation service that must respond within 1 second for short phrases. The service will handle thousands of requests per hour. Quality is important, but latency and cost are critical. Which Claude model is the most appropriate?

Accepted Answer

Claude 3 Haiku. Claude 3 Haiku is optimized for low latency and low cost, making it the best fit for real-time translation of short phrases. It can meet the 1-second response requirement and handle high throughput without excessive expense. More powerful models like Sonnet or Opus would add latency and cost without necessary quality gains for this straightforward task.

Answer

Claude 3 Opus

Answer

Claude 3.5 Sonnet

Answer

Claude 3 Sonnet

Question 8

A developer is building a customer-facing chatbot using the Claude API. The bot must respond within 1.5 seconds on average. The team initially selected claude-3-opus-20240229 for its high quality, but latency is consistently above 3 seconds. They need to reduce latency while maintaining acceptable response quality. Which action should the developer take?

Accepted Answer

Switch to claude-3-haiku-20240307 and evaluate response quality against the original model.. The chatbot's strict latency requirement makes model selection critical. Opus prioritizes quality over speed, while Haiku is optimized for fast responses. Switching to Haiku directly addresses the performance bottleneck. The other options either do not affect model inference speed or only change how output is delivered, not how quickly it is produced. The developer should then verify that Haiku's quality is sufficient for the use case.

Answer

Increase the max_tokens parameter to allow the model to generate more tokens per response.

Answer

Add a system prompt instructing Claude to respond as quickly as possible.

Answer

Enable streaming in the API request so that tokens are delivered incrementally.

Question 9

A startup is prototyping a chatbot that handles simple FAQ responses for a small user base. The team wants the lowest possible cost per request and does not need advanced reasoning. Which Claude model selection strategy is MOST appropriate?

Accepted Answer

Use a smaller, lower-cost model such as Claude Haiku, which is optimized for speed and cost on simpler tasks.. Matching model capability to task complexity is the core principle of cost-effective model selection. Simple FAQ responses do not need frontier reasoning, so the smallest and cheapest Claude model provides adequate quality at the lowest per-token cost. Larger models would increase spend without meaningful quality improvement for this workload.

Answer

Use a different provider's cheapest model since all providers offer equivalent FAQ performance.

Answer

Use the largest, most capable model to ensure the highest quality answers regardless of cost.

Answer

Use a mid-tier model for all requests and rely on prompt engineering to reduce token usage.

Question 10

A developer notices that a Claude integration's monthly bill is far higher than expected. Logs show that each request includes a 15,000-token system prompt containing detailed style guidelines, plus a 200-token user question, and the model generates a 50-token answer. The system prompt is identical across all requests. Which change would MOST reduce cost without altering output quality?

Accepted Answer

Enable Prompt Caching on the static system prompt so repeated requests pay a reduced cache-read rate for those tokens.. The dominant cost driver is the 15,000-token system prompt repeated on every request. Since it is static, Prompt Caching reduces the billed rate for those tokens on subsequent calls while keeping the prompt intact. This preserves output quality exactly and targets the largest cost component, making it the most effective change.

Answer

Shorten the system prompt by removing style guidelines that the model already follows by default.

Answer

Move the user question before the system prompt so the model processes it first.

Answer

Switch to a model with a smaller context window to force the system prompt to be truncated automatically.

Question 11

A developer is building a real-time chat application where users expect responses in under two seconds. The prompts are short (under 200 tokens) and the responses are typically one or two sentences. Which Claude model should the developer choose to optimize for latency and cost?

Accepted Answer

Claude 3 Haiku. For a real-time chat application with short prompts and simple responses, latency and cost are critical. Claude 3 Haiku is specifically designed to be the fastest and most affordable model in the Claude 3 family, making it ideal for this use case. More powerful models like Opus or Sonnet would add unnecessary cost and latency without providing meaningful benefits for such straightforward interactions.

Answer

Claude 3.5 Sonnet

Answer

Claude 3 Opus

Answer

Claude 2.1

Question 12

A team is estimating costs for a new feature that will send 1 million requests per month. Each request has a 2,000-token input and generates a 500-token output. The team wants to reduce the output token cost, which dominates the bill. Which strategy is MOST effective for reducing output token costs?

Accepted Answer

Instruct the model to be concise and set a lower max_tokens limit to cap response length.. Because output tokens dominate the monthly bill, the most effective lever is limiting how many tokens the model generates. Lowering max_tokens and prompting for conciseness directly caps output length and thus cost. Optimizing input tokens or context capacity addresses smaller or irrelevant components of the bill.

Answer

Enable Prompt Caching on the input to reduce the input token cost.

Answer

Increase the max_tokens parameter to allow longer responses.

Answer

Switch to a model with a larger context window to accommodate longer inputs.

Question 13

A developer is using the Claude Messages API to process a 50,000-token document for a summarization task. The summarization prompt and instructions add another 1,000 tokens. The developer wants to minimize output token costs while ensuring a comprehensive summary. Which strategy is most effective?

Accepted Answer

Craft a prompt that instructs Claude to produce a concise summary within a specified token limit, such as 'Summarize in under 500 tokens.'. To minimize output token costs while ensuring a comprehensive summary, the developer should control the output length through prompt instructions. Explicitly requesting a concise summary within a token limit guides the model to generate only the necessary content, reducing cost without sacrificing quality. Other strategies like chunking or using a smaller model may address different concerns but do not directly target output token reduction.

Answer

Split the document into smaller chunks and summarize each separately, then combine the summaries.

Answer

Set the max_tokens parameter to a very low value, such as 100.

Answer

Use a smaller model like Claude 3 Haiku for summarization.

Question 14

A developer is using the Claude API to generate marketing copy for 50 different product lines. Each request includes a unique set of product details and a unique creative brief. The developer wants to minimize cost while ensuring high-quality output. Which model should they choose?

Accepted Answer

claude-3-sonnet-20240229. Marketing copy generation requires creativity and coherence but not the deep reasoning of Opus. Sonnet provides a balanced combination of quality and cost, making it the most suitable for this task. Haiku might sacrifice quality, while Opus would be overkill and more expensive. Claude 2.1 is outdated. Therefore, Sonnet is the optimal choice to minimize cost while ensuring high-quality output.

Answer

claude-3-opus-20240229

Answer

claude-2.1

Answer

claude-3-haiku-20240307

Question 15

A developer is optimizing a Claude-powered document processing pipeline that sends large, mostly identical legal templates followed by short variable fields. They want to reduce input token costs while preserving output fidelity. Which TWO strategies are appropriate? (Choose two.)

Accepted Answer

Enable Prompt Caching on the static legal template so repeated requests reuse the cached prefix.. The two effective strategies are caching the static template and ordering the prompt so the stable prefix comes first. Caching reduces the billed rate for the large repeated content, and correct ordering ensures the cache key remains valid across requests. Together they cut input token costs while leaving the template content and output quality intact.

Answer

Remove the legal template entirely and rely on the model's pretrained knowledge of legal language.

Answer

Set max_tokens to a very low value to force the model to answer briefly.

Answer

Send only the variable fields and ask the model to reconstruct the template from memory.