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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?

A. Disable all hooks while the team re-scopes them, treating the period of no hook enforcement as a temporary state during the re-scoping work.
B. Scope hooks to only the high-risk actions, such as destructive operations or sensitive data access, and remove hooks from low-risk actions to balance safety with performance.
C. Replace hooks with system prompt instructions on the grounds that prompt instructions can produce the same enforcement effect that hooks produce on the agent's actions.
D. Disable the agent during peak hours so the hook overhead does not slow the application down during the busiest periods of the day across the application's operation.

Question 2

You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

A. A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.
B. The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
C. A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.
D. Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.

Question 3

Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.
B. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
C. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.
D. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.

Question 4

Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?

A. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
B. Notify downstream system owners that a change is coming and schedule the rollout for the following week, without conducting a formal impact assessment.
C. Document the prompt version changes in the application changelog and proceed with the rollout, treating the staging test results as sufficient evidence of impact across all downstream systems.
D. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.

Question 5

Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?

A. Retry the same request repeatedly until valid JSON appears in the model's response, with the retry loop adding delay to the application's response time on affected requests.
B. Add output validation that parses Claude's response against the expected schema and treats malformed output as a recognized error path with retry or fallback handling.
C. Manually inspect every response before downstream processing so a human reviewer catches any malformed JSON before the application passes the response to downstream systems.
D. Switch to free-form text output so the application no longer depends on JSON parsing for any of the responses it sends to downstream systems during normal operation.

Solutions: