CCDV-F practice questions: Model selection and optimization.

A pipeline extracts three fields from short emails, millions of times a month. Which model tier makes sense?

• The most capable tier, since extraction errors at this scale compound quickly
• A mid tier with extended thinking enabled to raise accuracy on ambiguous fields
• The tier with the largest context window, so that long quoted email threads are not truncated before extraction
• The smallest tier that passes your eval, since cost and latency dominate at this volume

You need the same input to produce the same extraction every time. Which setting helps most?

• Add a longer system prompt that describes each field in detail
• Set temperature near 1 so the model commits firmly to a single answer
• Set temperature near 0 so sampling favors the most likely tokens
• Enable prompt caching so the cached prefix yields the same output

Which is the best practice for choosing a model tier for a new feature?

• Use the largest tier for anything customer-facing and the smallest tier for internal tooling and scripts
• Pick the tier with the largest context window so prompts will not need trimming
• Prototype on a capable tier, then test whether a smaller tier passes your eval at lower cost
• Start on the smallest tier and move up only when users report errors

Output tokens dominate latency for a summarization endpoint. Which lever helps most?

• Enable prompt caching on the source document so the whole response is served faster
• Move the request to the Message Batches API for higher throughput
• Constrain the output length and format so the model writes fewer tokens
• Add extended thinking so the model plans before writing the summary

What does max_tokens control?

• The size of the prompt prefix that is eligible for caching
• The maximum number of output tokens the model may generate
• The number of tokens kept when the context window overflows
• The total tokens allowed across input and output for the request

A classification endpoint with a fixed label set should use which sampling setting?

• Low temperature so the same input maps to the same label
• A high top_p so rare labels are not excluded
• High temperature so the model explores less obvious labels
• Default temperature with top_k raised to widen the candidate set

When does extended thinking justify its cost?

• On every request, since thinking is cached and free after the first call
• On short factual lookups where the answer must be verified twice
• On streaming requests, where thinking fills time while text arrives
• On hard multi-step problems where accuracy matters more than latency

Your bill is dominated by a stable 20k-token system prompt sent on every call. First optimization?

• Add a cache_control breakpoint to the system prompt so its tokens are read from cache
• Move the prompt into the first user message so it is billed as one block
• Switch to a smaller model tier, since input tokens cost less there
• Send the requests through the Batch API to get the discounted rate

Which workload is a good fit for the Batch API?

• An IDE assistant that answers as the developer types
• A voice agent that needs turn-by-turn responses
• Nightly enrichment of a million records with no latency requirement
• A live customer chat reply that must appear on screen within a couple of seconds

What is a reasonable way to compare two models for your task?

• Run the same eval set through both and compare accuracy, cost, and latency
• Compare context window sizes and choose the larger one
• Ask each model to rate its own fitness for the task
• Pick the newer model, since each release is documented as improving on the last